Zepto, an innovative startup, revolutionizes grocery delivery with lightning-fast service, promising a 10-minute delivery timeframe. Zepto’s cutting-edge model relies on strategically located dark stores, strategically positioned across major Indian cities including Mumbai, Bangalore, Delhi, Noida, Ghaziabad, Gurgaon, Chennai, Pune, Kolkata, and Hyderabad.
Backed by substantial funding from both Indian and global investors, including prominent names like Y Combinator, Nexus Venture Partners, and Glade Brook, Zepto is poised for unprecedented growth. Zepto team comprises top talent sourced from industry giants such as Uber, Flipkart, and Dream11, as well as prestigious educational institutions like Stanford, INSEAD, IIM, and IIT. With this formidable combination of resources and expertise, Zepto is set to redefine convenience in grocery shopping nationwide.
Responsibilities as a Data Analyst
As an Analytics Specialist at Zepto, your role encompasses two key areas: Analytics and Growth.
In the Analytics realm, you’ll spearhead projects aimed at providing valuable insights to internal stakeholders. This involves crafting reports, dashboards, and automation scripts using tools like Google Suite, SQL, Tableau, R, and Python. Working closely with the City General Manager, you’ll ensure that data-driven decision-making becomes the norm within our organization.
Within the Growth domain, you’ll collaborate with cross-functional teams in Marketing, Procurement, Assortment Strategy, and Operations. Together, you’ll devise and implement growth strategies grounded in data analysis. This includes identifying and addressing process inefficiencies, leveraging automation to streamline operations, and conducting clean, data-backed experiments to refine our approach. At Zepto, we value ownership and encourage a culture where every team member is passionate about our mission and actively contributes to our collective success.
Requirements
Here are the requirements summarized:
Proficiency in SQL.
Experience with at least one data science programming language, preferably Python or R.
Ability to solve complex problems and propose innovative solutions.
Experience in developing and executing project proposals from start to finish.
Meticulous attention to detail.
Strong work ethic and ability to work with a sense of urgency.
Exceptional written and verbal communication skills.
A data-driven approach to decision-making and performance measurement.
Dedication to continuous improvement, both personally and for the business.
Technical proficiency in Google and/or Microsoft productivity tools.
Questions Asked
Operation questions like vlookups, Hlookups, xlookups etc,.
Reporting questions like pivot tables, typing speed, power pivots, pricing sheet preparation
SQL queries like joins, aggregation functions, filters etc,.
Advanced analytics and finding trends and patterns with MH – operations data
How fast can you prepare a bar chart with product operations data?
How can you merge multiple excel files together?
As a data analyst can you do data entry work also?
cumulative sum for given table
Joins, operations questions
Customer retention case study
Cumulative sum for given table
The Data Monk services
We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now
YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions Link –The Data Monk Youtube Channel
Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study Link –The Data Monk website
E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out Link –The Data E-shop Page
Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms Link –The Data Monk Instagram page
The world’s largest brewer is Anheuser-Busch InBev. Their vision is to be recognized as the Best Beer Company, bringing people together for a better world.
For centuries, the act of sharing a beer has been known to unite people. Even in today’s hyper-connected, always-on world, this simple gesture retains its significance. AB InBev, a multinational beverage and brewing company headquartered in Leuven, Belgium, is committed to driving growth that enhances the lives of individuals worldwide. This is achieved through brands and experiences that foster connectivity, dedication to brewing top-quality beer with premium ingredients, and support for the growth of farmers, retailers, entrepreneurs, and communities.
A company is being built to withstand the test of time, not just for a decade, but for the next 100 years. Through their brands and investments in communities, they aim to unite more people, ensuring that their company becomes an integral part of consumers’ lives for generations to come. Their diverse portfolio of over 400 beer brands includes globally recognized names like Budweiser, Corona, and Stella Artois, as well as international brands such as Leffe and Hoegaarde, along with local favorites like Haywards and Knockout. The tradition of brewing the finest beer stretches back over 600 years, spanning continents and generations.
Skills Required
SQL
Statistics
Python
Algorithms
Probability
Machine Learning
Analytics
A/B Testing
Questions Asked
SQL Questions
Question: The CFO of a large corporation wants to analyze the expenses in all departments for the previous financial year (2022). Write a SQL query to calculate the total expenditure for each department in 2022. Additionally, include the average expense across all departments in 2022 for comparison. Ensure that the output includes the department name, total expense, and average expense (rounded to 2 decimal places). Sort the data in descending order by total expenditure.
Input Tables:
departments table:
Column
Type
id
INTEGER
name
VARCHAR
expenses table:
Column
Type
id
INTEGER
department_id
INTEGER
amount
FLOAT
date
DATE
Output Table:
Column
Type
department_name
VARCHAR
total_expense
FLOAT
average_expense
FLOAT
Question: Design a function named rain_days to determine the probability of rain on the nth day following today, taking into account the current and previous day’s weather conditions. If it is raining both today and yesterday, there’s a 20% likelihood of rain tomorrow. If it rained on one of the past two days, there’s a 60% chance of rain tomorrow. If neither day saw rain, the probability of rain tomorrow is 20%.
Example: Input: n=5
Output:
def rain_days(n) -> 0.39968
Behavioral Questions
At Anheuser-Busch, we advocate for employees surpassing their personal benchmarks. How do you push your limits and strive for growth in your professional environment?
Discuss a recent setback you encountered at work and elaborate on the lessons you derived from it.
Describe a recent learning experience you encountered at work, specifically focusing on a situation where things didn’t go as planned. What insights did you gain from this experience?
What strategies do you employ to maintain your motivation, particularly during challenging times?
Anheuser-Busch values employees who embrace change and innovation. Can you provide examples of how you’ve successfully adapted to changes in your previous workplace?
Describe an instance where you spearheaded a workplace initiative or improvement.
Could you share any recent insights or information you’ve come across regarding one of Anheuser-Busch’s brands?
Should Anheuser-Busch consider incorporating nutritional information on its beer labels?
What strategies or approaches do you believe Anheuser-Busch could adopt to enhance innovation within the company?
Are there any inquiries you’d like to raise regarding this position or opportunities within Anheuser-Busch InBev?
The Data Monk services
We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now
YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions Link –The Data Monk Youtube Channel
Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study Link –The Data Monk website
E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out Link –The Data E-shop Page
Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms Link –The Data Monk Instagram page
Company – OYO Designation – Data Analyst Year of Experience required – 0 to 4 years Technical expertise – SQL, Python, Case Study, and Statistics Salary offered – 10 to 18 LPA (no Stocks, 10% variable) – 80% hike Number of Rounds – 4
There were 4 to 5 rounds in the recruitment process, the number of rounds depend on the candidate’s performance in the technical round.
Round 1 – Written SQL round Round 2 – SQL (based on the questions asked in the first round) Round 3 – Project-based questions and statistics (basic) Round 4 – Case Study Round 5 – Hiring Manager as well as case study
Round 1 – Written SQL round
There were 4 SQL questions(mostly from Hacker Rank) that need to be solved in 1 hour. Question split:- – 2 easy – 1 medium – 2 hard
For easy questions, concentrate on the basics of rank, lead, lag, and aggregate functions.
Round 2 – SQL Interview
This round was mostly around the written questions asked in the previous round and the approach of your solution. You need to have at least 3 correct answers to get to this round
Tips – Concentrate on communicating the approach, the questions in this round is completely on the 5 written questions, so you can revise the approach or concepts of these questions before the second round.
Round 3 – Project Based Questions and Statistics
I had a project in Machine Learning (ARIMA forecasting) so questions were mostly around the problem that we were trying to solve and some statistics concepts:- – What is the p-value? – What is correlation? Give an example of a negative correlation – Complete walk-through of the ARIMA model – What is multicollinearity? – Difference between regression and classification model – What is the degree of freedom?
Questions were mostly based on the type of project that you had written in your resume and the statistics or concepts associated with it.
So, for this round do prepare your project in as much detail as possible
Round 4 – Case Study
The technical rounds were the most important rounds. If you have performed decently in the first 3 rounds then there is a high chance of converting the role.
Case Study asked to me – How can Netflix increase its revenue by 50% in the next couple of years? It was a mix of guesstimates and business case studies.
So, I started with some approx numbers and their current split. For Example – Netflix has a total revenue of $100 Million and they are currently in 4 verticals and 10 countries. The current verticals are Hollywood, Bollywood, TV Series, and Other Country shows. The 10 countries are India, the USA, the UK, and 7 more small population countries. Assumption – India has 60% of the total revenue and 100% of the revenue is coming from Bollywood movies.
After a set of assumptions, we had to discuss the approach, the important points that we discussed were:- – Moving to or acquiring already performing OTT or their most-watched series – Advertisement to screen time ratio. To either increase the advertisement length or the frequency of it in a show or movie – Reducing the number of users that can use one subscription in parallel – Making a provision of taking the phone numbers that would be associated with one account at the time a user is buying the subscription. This will reduce the frequency of distribution of subscription
There were discussions on each of these points, you just need to bring as many diverse points in the discussion as possible. Do comment your approach in the comment box below.
Round 5 – Hiring Manager Round
This round was mostly around cultural fit wherein the candidate’s previous experience was checked along with the work culture he/she was working in. But, I was asked one more question i.e. to decide the price of a micro stay in OYO rooms. SO, OYO rooms were moving to a micro stay model where you can book a room for 6-12 hours, so the question was to have a dynamic rate charter for the booking of the room.
My approach was to have a Linear Regression model to get the rate of the room. And the independent variables that I suggested were:- – Daily price of the room – Day of booking – Price of the adjacent rooms – Time of booking – Customer Life Time Value who is booking the room – Number of rooms and number of booked rooms for that day – Holiday season impact
OYO SQL Interview Questions
There were 10+ SQL questions, 6-7 easy/theoretical , a couple of medium problem and 1 hard problem. The hard problem was picked directly from Hacker Rank, so practice all the problems. The medium difficulty problems were like the one give below:
Question 1:You have data on people have applied for a lottery ticket. The data consists of their name and ticketnumber. You have to choose winners by selecting the people present in the alternate rows (the first winner starting from row number 3). Write a query to make things easy to select the winners.
Answer:
select * from (select name, ROW_NUMBER() over (order by ticket_no) as srNo from db) t where (t.srNo % 2) = 1
Question 2:Find all the students who either are male or live in Mumbai ( have Mumbai as a part of their address). Answer:Select name From students Where lower(gender) in (‘male’,’m’) Or lower(address) = ‘%mumbai%’
Question 3:Can you join two table without any common column? Answer:Yes we can do cross join without any common column. Eg: We have Roll Number, Name of Students in Table A and their Class (let’s say 5th) in Table B. 21 We will use cross join to append class against each student.
SELECT B.CLASS,A.ID,A.NAME FROM A, B WHERE 1=1
Question 4:
Select case when null=null then ‘Amit’ else ‘Rahul’ end from dual. What will be the output of the above query? Answer:The Null value has a memory reference.2 Null values cannot have same memory Reference. So output will be ‘Rahul’.
Question 5: List the different types of relationships in SQL.
There are different types of relations in the database:
One-to-One – This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other.
One-to-Many and Many-to-One – This is the most frequent connection, in which a record in one table is linked to several records in another.
Many-to-Many – This is used when defining a relationship that requires several instances on each sides.
Self-Referencing Relationships – When a table has to declare a connection with itself, this is the method to employ.
Question 6: What are the differences between OLTP and OLAP?
Answer:OLTP stands for online transaction processing, whereas OLAP stands for online analytical processing. OLTP is an online database modification system, whereas OLAP is an online database query response system.
Question 7: What is the usage of the NVL() function?
Answer: You may use the NVL function to replace null values with a default value. The function returns the value of the second parameter if the first parameter is null. If the first parameter is anything other than null, it is left alone.
OYO Case Study Questions
Case Study – Suggest as many important KPIs as possible that you want to put on the CXOs dashboard
Following were the suggested KPIs
Average Daily Rate (ADR)
Occupancy rate
Revenue per Available Room (RevPAR)
Gross Operating Profit per Available Room (GOPPAR)
Average Length of Stay (ALOS)
Customer Acquisition Cost (CAC)
Customer Lifetime Value (CLV)
Net Promoter Score (NPS)
Online Reputation Score (ORS)
Room Revenue Contribution by Channel
Website Conversion Rate
Direct Booking Ratio
Repeat Guest Ratio
Housekeeping Productivity Ratio
Employee Turnover Rate
Revenue per Employee (RPE)
Cost per Occupied Room (CPOR)
Cost per Available Room (CPAR)
Total Revenue by Property
Total Expenses by Property
OYO Statistics and Python Interview Questions
I had a couple of projects on Machine Learning, so a few questions were asked on statistics
What is Skewness?
Skewness is a measure of the asymmetry of a distribution. This value can be positive or negative.
A negative skew indicates that the tail is on the left side of the distribution, which extends towards more negative values.
A positive skew indicates that the tail is on the right side of the distribution, which extends towards more positive values.
A value of zero indicates that there is no skewness in the distribution at all, meaning the distribution is perfectly symmetrical.
2. What is Kurtosis?
Kurtosis is a measure of whether or not a distribution is heavy-tailed or light-tailed relative to a normal distribution.
• The kurtosis of a normal distribution is 3.
• If a given distribution has a kurtosis less than 3, it is said to be platykurtic, which means it tends to produce fewer and less extreme outliers than the normal distribution.
• If a given distribution has a kurtosis greater than 3, it is said to be leptokurtic, which means it tends to produce more outliers than the normal distribution.
3.How are covariance and correlation different from one another?
Covariance measures how two variables are related to each other and how one would vary with respect to changes in the other variable. If the value is positive, it means there is a direct relationship between the variables and one would increase or decrease with an increase or decrease in the base variable respectively, given that all other conditions remain constant.
Correlation quantifies the relationship between two random variables and has only three specific values, i.e., 1, 0, and -1.
1 denotes a positive relationship, -1 denotes a negative relationship, and 0 denotes that the two variables are independent of each other.
4.What is Multicollinearity ?
Multicollinearity occurs when two or more independent variables are highly correlated with one another in a regression model. This means that an independent variable can be predicted from another independent variable in a regression model.
5.What is VIF?
Variance inflation factor (VIF) is a measure of the amount of multicollinearity in a set of multiple regression variables. In general, a VIF above 5 indicates high correlation and is cause for concern. Some authors suggest a more conservative level of 2.5 or above and it depends on the situation.
6.What is a confusion matrix and why do you need it?
Confusion matrix is a table that is frequently used to illustrate the performance of a classification model i.e., classifier on a set of test data for which the true values are well-known. It allows us to visualize the performance of an algorithm/model. It allows us to easily identify the confusion between different classes. It is used as a performance measure of a model/algorithm. It is summary of predictions on a classification model.
7.What do you mean when you say “Strings are immutable”?
Strings in Python are immutable i.e you can not change the defined string.
You can not change a part of the string, as it is immutable.
8.Are lists mutable ? Lists are mutable i.e. you can change the values already present in the list.
9.Is dictionary zero indexed? Can we pull something like Team[0] from the above example?
The whole purpose of having a dictionary is that you can have your own index i.e. key. So, to answer the question, Dictionary is not zero indexed.
You can not use the basic index thing example, you can not use Team[0] to pull the first value because you have already specified an index to all the values
10.What is the function range() ?
Range(10) will get you numbers from 0 to 9. But you need to put this range in some data type. Suppose you want to put this in a list.
There were a 2-3 more questions on Python, mostly around for loop and pattern printing.
The Data Monk services
We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now
YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions Link –The Data Monk Youtube Channel
Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study Link –The Data Monk website
E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out Link –The Data E-shop Page
Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms Link –The Data Monk Instagram page
The Atlassian interview process comprises various stages, starting with an initial phone screening and progressing through technical assessments, coding challenges, and multiple rounds of in-person or virtual interviews. These interviews encompass a mix of technical inquiries, behavioral assessments, and interactive discussions with potential team members or managers. Reflecting Atlassian’s emphasis on collaboration, the process may involve group activities or presentations to gauge candidates’ teamwork skills. Adequate preparation for both technical and behavioral questions is crucial, along with showcasing a solid understanding of Atlassian’s products and core values.
At Atlassian, proficiency in SQL is essential for extracting and analyzing user behavior data to improve software tools and for managing databases to ensure efficient retrieval and updating of product information. Consequently, SQL-related questions are a common feature in interviews for Data Science, Data Engineering, and Data Analytics positions at Atlassian.
If you’re preparing for a SQL interview, here are few Atlassian SQL interview questions for you to practice
Questions
Provide an example of ticket closure and outline the steps involved in conducting hypothesis testing for the given scenario.
What strategies would you employ to enhance the efficiency of the ticket closure process?
How would you address a situation where collaboration and support from colleagues and managers are lacking in your work environment?
SQL Questions
1. Why is the UNIQUE constraint used in SQL?.
Answer:
The UNIQUE constraint in SQL serves the crucial function of ensuring that all values within a specified column are distinct. This constraint is commonly utilized in conjunction with other constraints, such as NOT NULL, to impose specific rules on the data.
For instance, let’s consider a scenario in a university database where student information is stored:
In this example, the UNIQUE constraint is applied to the “student_email” field to guarantee that each student’s email address is unique. This helps maintain data integrity within the student_records table, preventing the possibility of having two students with the same email address. The UNIQUE constraint acts as a safeguard against potential errors and duplicates in the dataset, ensuring the reliability of the information stored.
2. Explain the concept of a database index and provide examples of various index types.
Answer: A database index is employed to enhance database performance by reducing the search scope required to retrieve a record.
There exist different types of indexes within a database:
Primary Index: Utilizes a unique identifier for direct row access.
Unique Index: Enforces uniqueness for the indexed columns in a table.
Composite Index: Established on multiple columns, expediting searches across various columns.
Clustered Index: Dictates the physical data order within a table.
3. Scenario: Calculate the Average Processing Time for Orders Across Regions
Imagine a scenario where a company manages orders across various regions using a database with two tables: regions and orders. The regions table contains information about different regions and their region_id, while the orders table includes details such as order_id, the region in which it was processed, and the timestamps for order creation and fulfillment.
Objective: Write a SQL query to determine the average processing time for orders in each region.
regions Example Input:
region_id
region_name
1
North Region
2
South Region
3
East Region
orders Example Input:
order_id
region_id
created_at
fulfilled_at
1
1
2021-07-05 08:00:00
2021-07-07 12:00:00
2
1
2021-07-06 10:00:00
2021-07-08 14:00:00
3
2
2021-07-07 12:00:00
2021-07-09 16:00:00
4
2
2021-07-08 14:00:00
2021-07-10 18:00:00
5
3
2021-07-09 16:00:00
2021-07-11 20:00:00
Answer: A SQL query can be formulated to join the two tables on region_id, calculate the time difference between fulfilled_at and created_at for each order, and then group by region_id to compute the average processing time.
Here’s a potential PostgreSQL query:
This query adapts the previous example to calculate the average processing time for orders in each region, with the output providing a list of regions and their corresponding average processing time in hours. Adjust the divisor as needed for different time units.
4. Scenario: Monthly Average User Engagement
Imagine Atlassian is keen on understanding user engagement across its products, Jira and Confluence. For this analysis, a database table named user_activity_logs is used, documenting entries whenever a user interacts with either product. The table comprises columns for log_id (a unique identifier), user_id (identifying the user), product (indicating the tool used – ‘Jira’ or ‘Confluence’), timestamp (the date and time of the activity), and duration (the time spent on the activity in minutes).
Objective: Construct a SQL query to determine the average user engagement duration for each product on a monthly basis for the year 2022.
user_activity_logs Example Input:
log_id
user_id
product
timestamp
duration
1001
101
Jira
01/05/2022 09:30:00
25
1002
102
Confluence
02/10/2022 12:45:00
40
1003
103
Jira
03/15/2022 14:00:00
20
1004
104
Confluence
04/20/2022 10:00:00
30
1005
105
Jira
05/25/2022 16:30:00
35
1006
106
Confluence
06/30/2022 11:00:00
15
1007
107
Jira
07/05/2022 13:45:00
18
1008
108
Confluence
08/12/2022 10:30:00
28
Example Output Table:
month
product
avg_duration
1
Jira
25.00
2
Confluence
40.00
3
Jira
20.00
4
Confluence
30.00
5
Jira
35.00
6
Confluence
15.00
7
Jira
18.00
8
Confluence
28.00
Answer: To determine the average user engagement duration for each product monthly in the year 2022, a SQL query can be crafted as follows:
This query filters activities from the year 2022, groups the data by the month and product, and calculates the average duration for each group. The final result is ordered by month and then product. Adjust column names and specifics based on the structure of your user_activity_logs table.
5. Scenario: Determining Most Frequently Used Software Tools
Imagine you have a database that tracks user interactions with various software tools provided by Atlassian. Your goal is to identify the software tool that was most frequently used over the last month.
To achieve this, you have a ‘User’ table storing user data and a ‘Usage’ table recording instances of users interacting with the software tools.
users Sample Input:
user_id
user_name
101
Alice
102
Bob
103
Carol
104
David
usage Sample Input:
usage_id
user_id
product_name
date
1
101
Jira
07/22/2022
2
102
Jira
07/15/2022
3
101
Confluence
07/02/2022
4
103
Bitbucket
07/15/2022
5
104
Trello
07/25/2022
6
103
Bitbucket
07/30/2022
7
101
Jira
07/19/2022
8
102
Jira
07/15/2022
9
103
Bitbucket
07/15/2022
10
101
Confluence
07/23/2022
Task: Write a query to retrieve the name of the software tool that was most frequently used in the last month and provide the total number of usages.
Answer:
This query groups records in the ‘usage’ table by ‘product_name’, counts occurrences for each group, and limits the result to the tool with the highest count. The WHERE clause ensures only records from the current month are considered. The EXTRACT function retrieves the month part of the date in PostgreSQL, and DATE_TRUNC returns the first day of the current month. If there are ties for the most frequent usage, this query will return one of them. If you want all usages in case of ties, you can remove the LIMIT clause.
6. Scenario: Analyzing Issue Resolution Duration
Imagine you’re tasked with analyzing issue resolution patterns for projects at Atlassian, a software development company that extensively uses issue tracking systems like Jira. Your objective is to determine the average duration in days it takes to resolve issues for each project.
issues Sample Input:
issue_id
project_id
status
reported_date
resolved_date
201
987
resolved
03/12/2022
04/05/2022
302
654
resolved
04/15/2022
05/10/2022
453
987
open
05/20/2022
null
604
321
resolved
07/01/2022
07/25/2022
705
654
resolved
08/10/2022
08/28/2022
806
321
resolved
09/05/2022
10/02/2022
Example Output Table:
project_id
avg_duration
987
24.50
654
18.50
321
19.00
Task: Write a query to calculate the average duration in days it takes to resolve issues for each project.
Answer:
In this query, we first filter issues by their resolution status, excluding those that aren’t resolved. The subtraction of reported date from resolved date calculates the duration it took to resolve each issue. The results are then grouped by project_id, and the AVG function computes the average duration for each project. The final output provides insight into the average resolution time for issues in different projects. Adjust the column names and specifics based on the structure of your actual dataset.
7. Scenario: Analyzing Monthly Sales per Product
In your role as a data analyst at Atlassian, you’re tasked with determining the average monthly sales per product to gain insights into product performance. This involves grouping the data by both month and product and calculating the average sales using the AVG function.
sales Sample Input:
sale_id
product_id
sale_date
unit_price
quantity
101
Jira
08/05/2022 12:00:00
120
4
102
Confluence
08/10/2022 14:30:00
180
3
103
Bitbucket
08/18/2022 11:45:00
40
8
104
Jira
09/12/2022 10:00:00
110
6
105
Bitbucket
09/22/2022 09:15:00
45
5
Example Output Table:
month
product
avg_sales
8
Jira
480
8
Confluence
540
8
Bitbucket
320
9
Jira
660
9
Bitbucket
225
Task: Write a query to calculate the average monthly sales per product.
This query first extracts the month from each sale date using the EXTRACT function. It then groups the data by month and product_id and calculates the average sales for each group using the AVG function. The ORDER BY clause ensures the results are ordered first by month and then by product for better readability. Adjust column names and specifics based on the structure of your actual dataset.
8. Scenario: Distinctive Features of Cross Join and Natural Join
In the realm of SQL JOIN operations, both cross join and natural join serve unique purposes. A cross join, also known as a cartesian join, combines every row from one table with every row from another, creating a new table. Conversely, a natural join merges rows from two or more tables based on shared columns, reflecting a natural relationship.
Example of Cross Join: Suppose you’re part of the Marketing Analytics team at <company_name>, tasked with correlating advertising copy and creative images. A cross join between the ad_copy and ad_creative tables could look like this:
This query generates combinations of each ad copy with every ad creative, producing a comprehensive set of possibilities.
Example of Natural Join: Consider sales data exported from Atlassian’s Salesforce CRM stored in a data warehouse with tables for sales and atlassian_customers. An INNER JOIN, a type of natural join, could link these tables based on the common customer_id field:
This query retrieves matching rows from both tables, using the shared customer_id as the linkage. Notably, natural joins necessitate common columns for merging.
Key Differences:
Common Columns Requirement:
Cross Join: Does not require any common columns between the tables being joined.
Natural Join: Requires shared columns for merging.
Resultant Table Size:
Cross Join: Can create large tables, especially if the input tables have a substantial number of rows.
Natural Join: Returns a table with rows equal to the number of matching rows in the input tables.
These differences underscore the distinct characteristics and use cases of cross joins and natural joins in SQL. Adjustments to the examples can be made based on your actual dataset structure and requirements.
9. Scenario: Customer and Product Analysis
In the realm of customer and product analysis, you have two tables – customers and products. The customers table contains customer_id, region, and signup_time, while the products table holds product_id, customer_id, and purchase_time. Your task is to craft a SQL query to identify the count of customers from each region who made their first purchase within one month of signing up.
customers Example Input:
customer_id
region
signup_time
101
APAC
2022-01-03 00:00:00
102
AMER
2022-02-08 00:00:00
103
EMEA
2022-03-12 00:00:00
104
AMER
2022-04-18 00:00:00
105
APAC
2022-05-25 00:00:00
products Example Input:
product_id
customer_id
purchase_time
1001
101
2022-01-25 00:00:00
2002
102
2022-03-10 00:00:00
3003
103
2022-03-20 00:00:00
4004
104
2022-04-05 00:00:00
5005
102
2022-04-10 00:00:00
Task: Compose a SQL query to determine the count of customers from each region who made their first purchase within one month of signing up.
Answer:
This query combines the customers and products tables based on customer_id, filtering rows where the purchase_time falls within one month of the signup_time. The data is then grouped by region, and the count of distinct customer_ids is calculated for each region. This provides insights into the number of customers from each region who made their initial purchase within the first month of signing up. Adjust the column names and specifics based on your actual dataset structure.
Behavioral Questions:
Describe a significant challenge you’ve faced in your life and discuss how you successfully navigated through it.
In what ways do you believe your personal values resonate with the culture at Atlassian?
Share an instance when you successfully managed a workplace conflict.
Describe a situation in which you anticipated potentially missing a deadline. How did you handle and resolve the challenge?
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Established in 2007, Flipkart, an e-commerce company, initiated its sales journey by offering online books. Over the years, it has expanded its product range to encompass over 100 million items across 120+ categories. With a user base exceeding 120,000 registered users, Flipkart achieved a significant milestone in 2016 by becoming the first Indian app to surpass 50 million downloads.
Skills Required for Data Analyst Role
Technical Skills:
Proficiency in Python, R, or any programming language with a strong foundation in Data Structures and Algorithms.
Acquired analytical skills and a willingness to learn Data Analytics.
Familiarity with the Software Development Life Cycle (SDLC) and a solid understanding of Structures and Database Management Systems (DBMS).
Expertise in working with Excel, including proficiency in Macros.
In-depth knowledge of Power Bi and QlikView.
Familiarity with Data Warehousing & Mining.
Hands-on experience with Microsoft Word and Microsoft PowerPoint.
Expertise in data modeling using Entity Relationship (ER).
Behavioral Skills:
A collaborative team player.
Strong communication skills with fluency in written and verbal English.
Logical thinking ability.
Capable of handling multiple tasks simultaneously.
Strong verbal and presentation skills.
Interview Process
Interviews at Flipkart may differ based on the role and team; however, Data Analyst interviews generally follow a standardized process covering specific question topics.
Aptitude Test Round – Basic logical and quantitative aptitude test
Technical Round – Questions will be asked from SQL, Python, Excel questions, power BI, also few will be asked related to regression, hypothesis.
Technical Round – DI, Probability, Statistics, Time & Work
Case Study Round – Case study based on your experience.
Hiring Manager Round – Personal & Cultural Fit
Questions Asked
Aptitude Test Round Questions-
The length of a rectangle is increased by 25% and its breadth is decreased by 20%. What is the effect on its area?
Three men start together to travel the same way around a circular track of 11 kilometres in circumference. Their speeds are 4, 5 and 8 kilometres per hour respectively. When will they meet at a starting point?
An order was placed for the supply of a carpet whose breadth was 6 m and length was 1.44 times the breadth. What be the cost of a carpet whose length and breadth are 40% more and 25% more respectively than the first carpet. Given that the ratio of carpet is Rs. 45 per sq m?
Technical Round Questions-
A can do a work in 15 days and B in 20 days. If they work on it together for 4 days, then the fraction of the work that is left is?
Data Interpretation – A bar graph was given and we were asked to solve some question by looking into bar graph
What is the ratio of the total sales of branch B2 for both years to the total sales of branch B4 for both years?
Total sales of branch B6 for both the years is what percent of the total sales of branches B3 for both the years?
Project – Brief discussion about the project with some basic question related to project.
What is Excel Array Formula?
The different classes of relations created by the technique for preventing modification anomalies are called?
When the values in one or more attributes being used as a foreign key must exist in another set of one or more attributes in another table, we have created a?
2 ways to extract unique values in excel
How to concatenate two string.
How joins the two tables.
what are the types of SQL command.
What is pivot table in SQL.
what is variable in python
Difference between list and tuple.
Hiring Manager RoundQuestions-
Can you describe a time when you needed to balance multiple projects?
what are your salary expectations?
What are your projects related to job role you have applied?
Why do you want to join Flipkart?
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India’s leading e-commerce company, Myntra, is dedicated to ensuring the accessibility of fashion and lifestyle products to everyone. Solutions are created by us that disrupt the ordinary and contribute to making the world a happier and more fashionable place.
A company that is consistently evolving into newer and better forms, seeks individuals who are prepared to evolve with it. From its origins as a customization company in 2007 to being at the forefront of technology and fashion today, Myntra is going places, and it is encouraged for individuals to join this journey.
Skills Required
Apart from qualifications, the essential skills required for a Data Analyst position at Myntra include:
Mastery of database fundamentals with a demonstrated ability to translate diverse business requirements into effective SQL queries.
Proficient skills in Excel and PowerBI at an advanced level.
Valued hands-on experience in R, Python, Tableau, Qlikview, and Data Studio, especially in roles related to customer growth or customer analytics.
Demonstrated adaptability and the ability to excel in a dynamic and fast-paced work environment.
A collaborative team player who is comfortable engaging with individuals from various professional backgrounds.
Interview Process
The Myntra interview process comprises the following stages:
Application and Resume Screening: Applicants submit their online applications, and HR or recruiters review them to confirm qualifications and experience.
Technical Assessment: Candidates undergo a technical assessment, which includes exercises in data analysis and SQL to assess their technical proficiency.
Technical Interviews: Shortlisted candidates participate in technical interviews, where their experience, problem-solving skills, and proficiency in tools such as Excel, PowerBI, R, and Python are evaluated by experienced data professionals.
Case Study/Scenario-Based Interviews: Some candidates are presented with a real-world data analysis problem or scenario. They are then asked to articulate their approach and methodology for solving it during the interview.
Final Round Interviews: In certain instances, there may be a final round of interviews with senior team members or management to assess a candidate’s strategic thinking and alignment with the company’s goals.
Questions Asked
Create the pivot table, sort the data in ascending order.
Use lookup with the product based given data and find the needed data.
Write SQL queries to perform operations such as joining, filtering, and aggregating data from multiple tables?
Describe your approach to utilizing data analysis for resolving business challenges and how you communicate your findings through the use of data visualization tools.
Which statistical methods and tools do you use in your data analysis practices?
Case Study Question: How many cars are sold in your city in a month?
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Walmart stands as one of the world’s leading discount department store chains, boasting a global presence with thousands of stores that provide a diverse array of products at budget-friendly prices. The company offers competitive salaries, attractive incentives like stock options and 401(k) matching, and the opportunity to tackle intriguing business challenges. With Walmart’s strategic emphasis on boosting online sales while maintaining its commitment to affordable pricing, the demand for data analysts has surged. These professionals play a crucial role in optimizing pricing strategies, enhancing operations and supply chain efficiency, establishing robust data architecture, and monitoring key success metrics. In this comprehensive interview guide, we will navigate you through the Walmart data analyst interview process, explore important questions, and provide valuable tips to help you secure your ideal position with the retail giant.
Nature of Questions Asked in Walmart Data Analyst Interviews
Walmart Data Analyst interviews are tailored to assess a combination of problem-solving abilities, critical thinking skills, and proficiency in essential technologies such as SQL and reporting tools. Familiarity with machine learning, statistics, and coding in languages like Python or R is essential, and experience with big data technologies is considered advantageous.
It’s crucial to align your preparation with the specific role you’re applying for, whether it’s related to product analysis, risk assessment, or staff analytics. The advertised position may require expertise in building data architecture, analyzing user behavior, or managing information security. For example, if the role is within the transportation analytics team, understanding business operations and solving supply chain case study problems should be part of your interview preparation.
A valuable tip is to thoroughly read the job description, gaining insights into your daily responsibilities, the tools you’ll be using, and the specific business challenges the team aims to address. This understanding will guide your interview strategy effectively. Additionally, Walmart provides a helpful guide on their careers page to assist candidates in excelling during the interview process.
Data Analyst Interview Process
The Walmart Data Analyst interview process is structured to assess candidates’ technical proficiency, critical thinking skills, and alignment with the company culture. The key stages include:
Preliminary Screening: Initiated by a recruiter, this step aims to understand the candidate’s background and potential fit for the role. It’s an opportunity for candidates to inquire about the position and strategically highlight their skills.
Technical Interviews: Following the screening, candidates undergo technical rounds via phone or video calls. Questions may cover SQL, Excel, Tableau, and include behavioral and case study inquiries. The focus is on evaluating both technical competence and problem-solving abilities.
Onsite Interview: Successful candidates from the technical interviews proceed to onsite interviews, typically with a panel from the intended team. This stage combines technical and behavioral questions, allowing the team to assess the candidate’s suitability for the specific role.
It’s important to note that while the overall interview process follows this general format, the questions asked are tailored to the specific role and team. The list of popular analyst questions provided below is derived from actual Walmart interviews and similar roles and companies. For additional preparation, candidates can explore a comprehensive collection of interview questions.
Behavioral Questions
During Walmart interviews, expect to encounter several behavioral questions designed to evaluate your soft skills, gauge your future performance, and assess your ability to collaborate and adapt to dynamic situations.
What draws you to our organization and why do you want to work with us?
Share an instance where you went above and beyond expectations in a project.
Describe your approach to resolving conflicts within a team.
How do you manage and prioritize multiple deadlines effectively?
SQL Interview Questions
SQL proficiency is a crucial requirement for the Walmart data analyst role, so thorough preparation for these questions is essential.
Create a SQL query to fetch the latest transaction for each day from a bank transactions table, which includes columns such as id, transaction_value, and created_at representing the date and time for each transaction. Ensure the output contains the ID of the transaction, the transaction datetime, and the transaction amount, with transactions ordered by datetime.
Develop a SQL query to assess user ordering patterns between their primary address and other addresses. Provide a solution based on tables containing transaction and user data.
As the accountant for a local grocery store, you’re assigned the responsibility of determining the cumulative sales amount for each product since its last restocking. Utilizing three tables – products, sales, and restocking – where products provide information about each item, sales document sales transactions, and restocking tracks restocking events, compose a SQL query to fetch the running total of sales for each product since its most recent restocking event.
Formulate a SQL query to pinpoint customers who conducted more than three transactions in both the years 2019 and 2020. Emphasize the logical condition: Customer transactions > 3 in 2019 AND Customer transactions > 3 in 2020.
Write a SQL query to retrieve neighborhoods with zero users based on two provided tables: one containing user demographic information, including the neighborhood they reside in, and another dedicated to neighborhoods. The goal is to identify and return all neighborhoods that currently have no users.
Coding Questions
Explain the implementation of k-Means clustering using Python
Provide a comprehensive guide on constructing a logistic regression model in Python.
Describe the process of reconstructing a user’s flight journey.
Create a function to extract high-value transactions from two provided dataframes: transactions and products. The transactions dataframe includes transaction IDs, product IDs, and the total amount of each product sold, while the product dataframe contains product IDs and corresponding prices. The objective is to generate a new dataframe containing transactions with a total value surpassing $100, and to include the calculated total value as a new column in the resulting dataframe.
Describe the approach to identify the longest substring within a given string that exhibits maximal length.
Case Study Interview Questions
Outline the process for forecasting revenue for the upcoming year.
Describe the steps you would take to address the issue of underpricing for a product on an e-commerce site.
Which key performance indicators (KPIs) would you monitor in a direct-to-consumer (D2C) e-commerce company?
Outline the process of architecting end-to-end infrastructure for an e-commerce company.
What approach would you take to identify the most profitable products for a Black Friday sale, optimizing for maximum profit?
Statistics and Probability Interview Questions
Walmart data analysts frequently engage in quantitative tasks such as statistical modeling, sampling, and extensive analysis of datasets, charts, and model metrics. Possessing robust quantitative skills, especially in statistics and probability, is crucial for excelling in these responsibilities.
Walmart aims to assess customer satisfaction with a recently introduced in-store service. Outline your approach to crafting a survey that ensures a representative sample of customers. Additionally, explain the choice of sampling techniques and their rationale.
What is the drawback of the R-squared (R^2) method when analyzing the fit of a model that aims to establish a relationship between two variables. Discuss the limitations of the R-squared metric, situations in which it is appropriate, and propose alternative strategies. Support your response with examples.
Walmart is interested in examining whether there is a substantial disparity in customer spending between weekdays and weekends. Describe the statistical test you would employ for this analysis and elucidate your approach to interpreting the outcomes.
Outline strategies to minimize the margin of error in a study with an initial sample size of n, where the current margin of error is 3. If the goal is to reduce the margin of error to 0.3, discuss the additional samples required for this reduction. Emphasize the importance of seeking clarifications about the business context and explicitly state any assumptions made, as deviations can impact the margin of error.
Elaborate on the distinctions between a normal distribution and a binomial distribution. Offer instances where each distribution is relevant within a retail context, illustrating their applicability.
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YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions Link –The Data Monk Youtube Channel
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E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out Link –The Data E-shop Page
Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms Link –The Data Monk Instagram page
What does it take to become a data analyst at Adobe, a global software company headquartered in San Jose, CA, renowned for its digital media apps and marketing tools? With a diverse user base spanning millions worldwide, Adobe’s data analysts play a crucial role in deciphering customer data to enhance products, refine marketing strategies, and boost sales performance.
Adobe hires a broad spectrum of data analysts, encompassing roles such as business intelligence analysts, financial analysts, and web analytics analysts. These professionals have the opportunity to contribute to a myriad of projects and teams, spanning finance and operations, marketing, and product development. Boasting major hubs in Seattle, San Francisco, Austin, and New York, as well as a presence in India, Adobe’s workforce exceeds 24,000 employees globally.
For those aspiring to join Adobe as a data analyst, it is advantageous to familiarize themselves with the company’s divisions and teams. Understanding specific roles and responsibilities is crucial, as is gaining insights into the interview process for data analysts at Adobe.
Data Analyst Interview Process
This is a consistent reviews of interview experiences, revealing that Adobe’s data analyst interviews prominent feature:
SQL Interview Questions (Encountered in 94% of interviews)
Product Case Questions
Adobe Analytics
Excel Questions
Databases and Data Modeling
Machine Learning
Data Visualization (Emphasizing tools like Tableau and PowerBI)
The interview process for an Adobe data analyst role involves several stages:
HR Phone Screen: A brief conversation with a recruiter to discuss your past analytics projects, experience, and reasons for seeking an analyst position at Adobe.
Technical Screen: A technical call with an Adobe manager. Expect questions that are both behavioral and centered around intermediate SQL skills. Tip: Incorporate Adobe’s core values into your responses when discussing past experiences and projects.
Onsite Interview:
The onsite interview consists of multiple components:
Two live coding rounds, primarily focused on SQL coding.
Rounds covering web analytics, visualization, and case studies. These may involve SQL, analytics, or product-related scenarios.
Questions & Answers
What role does Adobe Analytics play, and what is its key function?
Answer: Adobe Analytics, a product crafted by Adobe, serves as a potent platform specializing in web and mobile analytics. Its primary function is to meticulously track, analyze, and furnish valuable insights into user interactions on digital platforms. This capability enables businesses to formulate and refine data-driven marketing strategies, enhancing the optimization of their digital assets.
What methods does Adobe Analytics utilize to capture user data on websites and applications?
Answer: Adobe Analytics employs JavaScript code on webpages and Software Development Kits (SDKs) in mobile applications to track user data. This involves capturing various user interactions such as page views and clicks, with the collected data transmitted to Adobe’s servers for in-depth analysis.
Define conversion events within Adobe Analytics and elucidate their significance.
Answer: In Adobe Analytics, conversion events refer to distinct actions or achievements by users on a website or app, such as completing a purchase. These events hold significance as they serve as measurable indicators of success, playing a pivotal role in optimizing user journeys and enhancing overall performance.
Which Adobe marketing solutions seamlessly integrate with Adobe Analytics?
Answer: Adobe Analytics harmoniously integrates with various Adobe marketing solutions, including Adobe Target for personalization, Adobe Campaign for marketing automation, and Adobe Audience Manager for precise audience segmentation and targeted marketing efforts.
What is the significance of “fallout” in Adobe Analytics, and how does it contribute to optimization?
Answer: In Adobe Analytics, “fallout” is a concept that monitors user drop-off at distinct stages within a defined user journey or funnel. Its importance lies in pinpointing precisely where users abandon the process, offering valuable insights for optimizing conversion paths and improving overall user engagement.
Why is monitoring “page views” essential in Adobe Analytics?
Answer: In Adobe Analytics, tracking “page views” holds significance as it quantifies the frequency with which users access specific pages. This metric offers insights into the popularity of content, user engagement levels, and plays a crucial role in optimizing website structure and content strategy.
Elaborate on the distinction between dimensions and metrics in Adobe Analytics.
Answer: In Adobe Analytics, dimensions are descriptors or labels that offer context to data, examples include page names or traffic sources. On the other hand, metrics are numerical values that quantify user actions, encompassing metrics like page views or conversion rates.
Highlight the significance of segmentation in Adobe Analytics and outline the process of creating a segment.
Answer: Segmentation in Adobe Analytics is crucial for analyzing data based on user-defined criteria, unveiling valuable insights into user behavior patterns. To craft a segment, users articulate rules by specifying dimensions, metrics, and employing logical operators within the interface.
Define “props” and “eVars” in Adobe Analytics and elucidate their respective use cases.
Answer: In Adobe Analytics, “props” refer to traffic variables, and “eVars” signify conversion variables, both capturing data attributes. “Props” are suitable for temporary traffic data, while “eVars” excel in capturing persistent conversion data. This distinction aids in effective analysis and reporting within the platform.
Explain the process of establishing a funnel analysis in Adobe Analytics and the insights it can provide.
Answer: To initiate a funnel analysis in Adobe Analytics, outline sequential steps in a user journey. The resulting insights encompass identifying drop-off points, assessing conversion rates, and uncovering opportunities for enhancing the user experience and optimizing conversion paths.
What is the concept of “calculated metrics” in Adobe Analytics, and could you offer an illustration?
Answer: In Adobe Analytics, calculated metrics are bespoke metrics created through mathematical expressions applied to existing metrics or dimensions. An example involves calculating the Average Revenue Per User (ARPU) by dividing the “Total Revenue” metric by the “Total Unique Visitors” dimension. These calculated metrics offer more profound insights into the data.
Explore the concept of attribution models in Adobe Analytics and outline prevalent models.
Answer: Attribution models in Adobe Analytics govern the allocation of credit for conversions to various marketing touchpoints. Common models encompass “first touch,” “last touch,” “linear,” “time decay,” and “custom” models, each providing distinct insights into user behavior throughout the conversion journey.
Explain the process of incorporating offline data into Adobe Analytics for a more comprehensive understanding of user behavior.
Answer: To integrate offline data with Adobe Analytics, leverage tools like Data Sources or Data Feeds. Import data from sources like CRM systems, call centers, or in-store purchases into Adobe Analytics, enabling a holistic comprehension of user behavior across both online and offline channels. This integration enhances insights and facilitates informed decision-making.
Define pathing analysis in Adobe Analytics and elucidate its role in enhancing user journeys.
Answer: Pathing analysis within Adobe Analytics involves scrutinizing the sequences of pages or events users engage with, unveiling prevalent user journeys. This analytical approach aids in optimizing user experiences by pinpointing preferred paths and highlighting areas that require enhancement for a more seamless and efficient user journey.
Explain the functioning of data processing in Adobe Analytics and the significance of data processing rules.
Answer: In Adobe Analytics, data processing encompasses the collection and organization of data stemming from user interactions. Data processing rules play a crucial role in customization, allowing actions such as filtering, enriching, or transforming data to ensure its accuracy and relevance for subsequent analysis.
Describe the role of “success events” in Adobe Analytics and outline the process of their configuration.
Answer: In Adobe Analytics, “success events” are instrumental in monitoring particular user interactions or achieving predefined goals, such as form submissions or video views. To set them up, articulate event parameters and employ Adobe’s tracking code or Software Development Kits (SDKs) to transmit data when the event occurs. This enables thorough performance analysis and facilitates optimization based on the collected data.
Explain the Adobe Analytics JavaScript library and the process of implementing it on a website.
Answer: The Adobe Analytics JavaScript library serves as tracking code offered by Adobe for website analytics. To deploy it, integrate the provided code snippet into the HTML of your website’s pages, usually within the <head> section. This insertion enables the collection and transmission of user data to Adobe Analytics servers for comprehensive website analytics.
What is the process for incorporating Adobe Analytics tracking in a single-page application (SPA)?
Answer: To implement Adobe Analytics tracking on a single-page application (SPA), leverage the Adobe Launch platform. Configure Adobe Analytics extensions within Launch to transmit data when there are changes in SPA routes or user interactions, ensuring precise tracking of user behavior within the single-page application.
Explain the purpose of “data sources” in Adobe Analytics and the scenarios that warrant their utilization.
Answer: In Adobe Analytics, “data sources” facilitate the integration of external data, providing added context for analysis. They prove valuable when there is a necessity to amalgamate data from diverse systems, such as CRM, email campaigns, or offline sales. This integration enhances the overall understanding of user behavior and performance, offering a more comprehensive perspective.
Explain the contrast between “first touch” and “last touch” attribution models in Adobe Analytics.
Answer: In Adobe Analytics, the “first touch” attribution model attributes conversions to the initial user interaction, like clicking an ad. Conversely, the “last touch” attribution model allocates credit to the final interaction preceding a conversion, such as a purchase. These models provide unique perspectives on the customer journey by emphasizing different touchpoints in the conversion process.
What is the purpose of the Saint API?
Answer: The Saint API is utilized for importing meta-data, which is employed to describe and group reporting values in Adobe Analytics.
What role does the Data Insertion API serve in Adobe Analytics, and when is it employed?
Answer: The Data Insertion API in Adobe Analytics facilitates the automated transmission of data to Adobe’s servers. It is utilized when there is a need to automate data uploads from external sources, such as CRM systems. This ensures a smooth integration of offline or third-party data, enabling comprehensive analysis and reporting within the Adobe Analytics platform.
What methods can be employed in Adobe Analytics to assess the success of a marketing campaign?
Answer: In Adobe Analytics, evaluating the effectiveness of a marketing campaign involves tracking campaign-related metrics, establishing conversion goals, and crafting custom segments to isolate campaign traffic. Utilizing reports and visualizations, one can measure crucial performance indicators such as traffic, conversions, and bounce rates. These insights contribute to refining and optimizing future campaigns for greater impact.
Explain the role of “data feeds” in Adobe Analytics and elucidate their functioning.
Answer: In Adobe Analytics, “data feeds” serve the purpose of offering raw data exports for in-depth analysis. They operate by consistently exporting data into flat files, such as CSV, encompassing dimensions, metrics, and events. This exported data can then be imported into external tools or databases, facilitating comprehensive analysis, modeling, or integration with other data sources.
What is the significance of JavaScript variables, such as props and eVars, in user data tracking.
Answer: JavaScript variables, including props and eVars, play a crucial role in capturing and transmitting data to Adobe Analytics. Props, identified as traffic variables, are employed for short-term tracking purposes. In contrast, eVars, recognized as conversion variables, persist across visits, facilitating detailed analysis and reporting of user behavior and conversion events.
What is the process of generating custom reports in Adobe Analytics, and what customization options are available?
Answer: To craft custom reports in Adobe Analytics, utilize the Adobe Workspace interface. Customization options encompass selecting dimensions and metrics, applying segments, specifying date ranges, and arranging report elements such as tables, charts, and visualizations to tailor the analysis according to your specific requirements.
Describe the process of employing the Adobe Debugger Chrome extension for diagnosing issues in Adobe Analytics implementations.
Answer: For troubleshooting Adobe Analytics implementations using the Adobe Debugger Chrome extension, start by opening a webpage, enabling the extension, and examining network requests. The extension reveals Adobe Analytics tracking calls, enabling verification of data accuracy, variable values, and identification of any errors for debugging and validation purposes.
Explain the steps for implementing cross-device tracking in Adobe Analytics.
Answer: To establish cross-device tracking in Adobe Analytics, configure a distinctive visitor ID, such as Customer ID or Experience Cloud ID, ensuring persistence across devices. Leverage Adobe’s visitor stitching capabilities or ID service to link user data as they engage with your digital assets, facilitating a cohesive view of user behavior across various devices.
What strategies can be employed to avoid data sampling in Adobe Analytics when handling extensive data sets?
Answer: To mitigate data sampling in Adobe Analytics with large data sets, explore options such as narrowing the date range, segmenting data, or utilizing report suites with elevated processing limits. Additionally, Adobe provides alternatives like Data Warehouse and Data Feeds for accessing unsampled data.
The Data Monk services
We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now
YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions Link –The Data Monk Youtube Channel
Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study Link –The Data Monk website
E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out Link –The Data E-shop Page
Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms Link –The Data Monk Instagram page
Microsoft has been dominating headlines for its significant venture into AI, a move that has propelled it to the top of the world’s company valuations in 2024, surpassing Apple. Beyond AI, Microsoft is poised to make substantial progress in various sectors such as gaming and cloud computing this year. The expansion of these businesses will necessitate an increased workforce to analyze datasets and discern trends, and this is where Microsoft Data Analysts will play a crucial role.
Microsoft, renowned for its generous compensation, favourable work-life balance, and comprehensive health benefits, is positioned as an employer offering both financial rewards and flexibility. This comprehensive guide is designed to lead you through the Microsoft Data Analyst interview process. It includes carefully selected questions, strategies for addressing them, and valuable tips to equip you for success. By the end of this resource, you will gain a thorough understanding of what to expect in Microsoft’s interview process.
ABOUT INTERVIEW PROCESS
The Microsoft Data Analyst interview process centers around evaluating candidates with proficiency in SQL and BI tools. Beyond data retrieval and reporting skills, Microsoft seeks analysts with a sharp ability to detect anomalies, identify patterns, and offer actionable insights. Given the significance of cultural fit, it is crucial to practice answering behavioural questions.
It’s essential to recognize that the interview questions and structure may vary depending on the specific team and function outlined in the job description. Thoroughly reviewing the job role is recommended when formulating your interview preparation strategy.
Microsoft’s interview process is known for its swiftness, often concluding within a few weeks.
The round details are as follows-
Preliminary Screening
Technical Assessment
Panel Interview
Final Interview
Preliminary Screening
Following your application, a recruiter from Microsoft will initiate a call to assess your professional background and evaluate your alignment with the company’s culture. Expect inquiries about your motivation for joining Microsoft, along with a few questions related to your resume. It’s advisable to prepare well-thought-out responses based on your research and previous projects to effectively communicate your qualifications and enthusiasm for the role.
Technical Assessment
Candidates who progress successfully are typically subjected to one or two technical interviews, commonly conducted through video chat. These interviews often involve a live coding round, where candidates collaborate on a shared whiteboard to demonstrate their technical skills.
Panel Interview
If there is a strong alignment, you will receive an invitation to an onsite visit where you’ll meet your prospective team and participate in a panel interview. This stage typically encompasses a blend of technical, behavioural, and case study questions.
Final Interview
In the concluding phase, you will likely have meetings with senior-level executives or team leaders. This final round is designed to evaluate your compatibility with the company culture and gauge your enthusiasm for becoming part of the organization.
Questions Asked
Develop a query to address a bug in our dataset, where duplicate rows have been identified. The objective is to identify and select the five most expensive projects based on the ratio of budget to employee count. Your query should account for and handle the presence of duplicate entries in the table.
This question holds significance in a Microsoft interview, given the involvement of Data Analysts with intricate datasets from diverse sources. It evaluates your capacity to ensure data integrity, a critical factor for effective decision-making in Microsoft’s realms of product development, market analysis, and operational efficiency.
How to Respond:
Illustrate your proficiency in SQL commands for eliminating duplicates and computing ratios.
Example:
“To address this, I would initiate a subquery to identify and eliminate duplicate rows. Utilizing the rank function over row number, I would then select the top five values. This approach ensures that all top budgets with the same value are retained, as row number effectively filters out duplicates.”
2. In the context of a Microsoft interview, where Data Analysts handle intricate datasets from various sources, this question evaluates your capability to uphold data integrity. Explain how you would handle the presence of duplicate rows while selecting the top five projects based on the budget-to-employee count ratio.
Answer:
To address this scenario, I would initiate a subquery to identify and eliminate duplicate rows. Utilizing the rank function over row number, I would then select the top five values based on the budget-to-employee count ratio. This ensures that all top budgets with the same value are retained, as the row number function effectively filters out duplicates, demonstrating my proficiency in maintaining data integrity.
3. In a Microsoft interview, where collaboration with colleagues of varying statistical backgrounds is common, articulate how you would convey the concept of a p-value to a non-statistician. The objective is to assess your ability to effectively communicate statistical ideas.
How to Respond:
Illustrate the process of statistical testing using the null and test hypotheses with a real-world example. Rather than a direct definition of the p-value, provide context that is relatable to someone without a strong statistical background.
Example:
“Imagine we have a new algorithm that we want to test for improved performance. We set up two hypotheses: the null hypothesis suggests the new algorithm doesn’t perform better, and the test hypothesis claims it does. The p-value comes into play by giving us a measure of how likely we would observe the same results if the new algorithm didn’t actually enhance performance. In simpler terms, it helps us decide whether the new algorithm is truly better. We typically reject the null hypothesis if the p-value is less than 0.05, indicating a statistically significant difference.”
4. In a Microsoft interview, where SQL proficiency is crucial for roles involving decision-making on subscription-based services like their cloud platform, demonstrate how you would find the average number of downloads for free versus paying accounts, broken down by day, using the given tables: accounts and downloads.
How to Respond:
Detail a SQL query that incorporates a join between the accounts and downloads tables. Your response should include grouping the results by account type and date, and calculating the average downloads for each group.
Example:
“To address this, I’d construct a SQL query that joins the accounts and downloads tables based on the account ID. Subsequently, I would group the results by both account type and the date of download. Employing the AVG function, I’d calculate the average number of downloads for each distinct group. This query would comprise a SELECT statement for account type and date, a JOIN clause to merge the tables, a GROUP BY clause for account type and date, and the AVG function to determine the average downloads.”
5. In a Microsoft interview, where the ability to make data-driven predictions based on incomplete information is crucial, consider a scenario where you are about to travel to Seattle and consult 3 randomly selected friends living there about the weather. Given that each friend has a 2⁄3 chance of telling the truth and a 1⁄3 chance of deceiving, and all 3 friends claim it’s raining, what is the probability that it is indeed raining in Seattle?
How to Respond:
Elaborate on Bayes’ Theorem and articulate the process of calculating the probability in this context.
Example:
“Applying Bayes’ theorem, considering a 50% chance of precipitation in Seattle, the probability that it is actually raining (P(Raining)) is calculated to be 8⁄9 or 89%. This calculation takes into account the likelihood of each friend telling the truth or lying, providing a data-driven prediction based on the incomplete information received.”
6. In a Microsoft interview, where expertise in preprocessing data for building reliable predictive models is crucial, imagine developing a model to forecast real estate home prices in a city. Upon analysing the home price distribution, you observe a right-skewed pattern. Should any actions be taken or considerations made in this scenario?
How to Respond:
Explain the implications of a right-skewed distribution and discuss how it might impact the model’s performance. Explore potential methods to transform or normalize the data to enhance the accuracy of the predictive model.
Example:
“When dealing with a right-skewed distribution in home prices, it indicates that a majority of homes have lower values, with fewer properties having extremely high prices. This skewness can potentially affect the performance of predictive models, which often assume a normal distribution of data. To address this issue, I would propose applying a logarithmic transformation to the home prices. This transformation helps normalize the distribution, mitigating the impact of skewness and improving the accuracy of the predictive model.”
7. In a Microsoft interview, where the ability to interpret data beyond surface-level observations is critical, consider a situation where approval rates for products have decreased from 85% to 82%, indicating a statistically significant drop. Despite individual analysis showing flat or increased rates for each product, the overall approval rate has declined. What might be the underlying cause?
How to Respond:
Discuss the concept of Simpson’s Paradox, emphasizing how aggregated data may present different trends from individual data. Propose investigating the distribution of applications across different products and how changes in this distribution might impact the overall approval rate.
Example:
“This scenario aligns with Simpson’s Paradox, where aggregated data can diverge from trends observed in individual groups. The decrease in overall approval rate, despite individual products displaying stable or increased rates, could be attributed to a shift in the volume of applications for each product. For instance, if a product with a lower approval rate experienced a substantial increase in applications, it could pull down the overall approval rate, even if the rates for each product individually remained constant or improved.”
8. In a Microsoft interview, where the ability to make quick estimates about large datasets is crucial, imagine being tasked with estimating the cost of storing Google Earth photos each year. This question aims to evaluate your back-of-the-envelope calculation skills, vital for project planning.
How to Respond:
Methodically break down the problem, make reasonable assumptions about unknown variables, and estimate the key components: the size of an average Google Earth photo, the total number of photos, and the storage cost per gigabyte. The interviewer is interested in your structured thought process.
Example:
“Let’s make an assumption that the average size of a high-resolution Google Earth photo is around 2MB. Given that Google Earth covers the entire Earth’s surface, roughly 510 million square kilometers, and assuming one photo per square kilometer, we arrive at 510 million photos. Annually, this translates to 1.02 terabytes of data. Now, assuming a storage cost of $0.02 per GB per month, the estimated cost would be approximately $20,400/month or around $244,800/year. It’s important to note that this is a simplified calculation, and the actual cost might be higher, considering factors like data redundancy, varying resolutions in certain areas, and ongoing updates.”
9. In a Microsoft interview, where algorithmic thinking is paramount for tackling scenario analysis and predictive modelling problems, consider a task involving determining the number of paths from the top left corner to the bottom right in an n×n grid. The goal is to assess your ability to devise efficient algorithms.
How to Respond:
Explain the combinatorial nature of the problem, emphasizing the permutations involved in choosing when to move right or down in the grid.
Example:
“In an n×n grid, navigating from the top left to the bottom right corner involves making a sequence of right (R) and down (D) moves. The total number of paths corresponds to the ways these 2n moves (n R’s and n D’s) can be arranged. This is essentially the binomial coefficient, representing the number of ways to select n positions for either R or D out of 2n total moves. In simpler terms, it’s the combination formula: C(2n, n) = (2n)! / (n! * n!). This formula efficiently calculates the number of paths in the grid.”
10. In a Microsoft interview, where understanding combined probabilities from independent events is crucial for roles involving product development and user experience optimization, consider a scenario where two algorithms, A and B, are being tested for a new search feature in Outlook. Algorithm A has a 60% chance of returning relevant results, and Algorithm B has a 70% chance. If a user randomly selects one of the algorithms for their search query, with an equal likelihood of choosing either, what is the probability that the user gets a relevant result?
How to Respond:
Explain that this is a problem involving combined probabilities from independent events, where two algorithms contribute to the desired outcome. Calculate the probability of obtaining a relevant result with each algorithm and then determine the weighted average based on the likelihood of each algorithm being chosen.
Example:
“The overall probability is derived from the sum of the probabilities of selecting each algorithm and obtaining a relevant result. For Algorithm A, this is calculated as 0.5 x 0.6, and for Algorithm B, it is 0.5 x 0.7.
Hence, the probability that the user gets a relevant result is (0.5 x 0.6) + (0.5 x 0.7) = 0.65 or 65%.”
11. In a Microsoft interview, where proficiency in Power BI is vital for roles involving data analysis, imagine the task of designing a Power BI dashboard to monitor the performance metrics of Microsoft 365 services across different regions. The aim is to evaluate your ability to present complex data in an accessible manner, considering the nuances of Microsoft 365 services.
How to Respond:
Discuss the relevant KPIs for tracking Microsoft 365 service performance, explaining your selection criteria. Describe how you would structure the dashboard for clarity and user-friendliness, showcasing your knowledge of Power BI functions. Consider tailoring the dashboard or incorporating layers based on the end user’s needs.
Example:
“Key performance indicators (KPIs) like user engagement, service uptime, incident reports, and regional usage statistics would be essential for tracking Microsoft 365 service performance. The dashboard would feature a user-friendly layout, incorporating an interactive map for visualizing regional data. Each region would be clickable, revealing detailed metrics such as active users, popular services, and ongoing issues. To enhance usability, I’d include filters for stakeholders to customize views by period, service type, or other relevant dimensions. Real-time updates would be integrated, and multiple tabs would cater to different end users. For instance, a senior executive’s view would prioritize the most crucial insights for effective decision-making.”
12. In a Microsoft interview, where optimizing SQL queries for large datasets is critical, consider a scenario where you are tasked with improving query performance. This question aims to assess your knowledge of SQL optimization, a key skill for efficiently handling data at Microsoft.
How to Respond:
Discuss optimization strategies, including indexing, query restructuring, appropriate join types, and minimizing unnecessary columns in the SELECT statement. Stress the significance of understanding the data structure and the specific business use case to determine the most effective techniques.
Example:
“To enhance the performance of a SQL query for a large dataset, I would initiate the process by examining the query execution plan, focusing on the business use case to identify potential bottlenecks. If the query involves joins, I’d prioritize efficient join order based on the size of the datasets. Creating indexes on columns used in WHERE clauses and JOIN conditions would be a key strategy for speeding up searches. I’d also be cautious about selecting unnecessary columns, particularly in large tables, and would leverage WHERE clauses to filter data at an early stage in the query. Considering the specific scenario, I might explore the use of subqueries or temporary tables if they contribute to a more efficient query.”
13. In a Microsoft interview, where A/B testing is a fundamental skill for various scenarios like implementing new product features, envision a situation where your team has implemented two different layouts for the Bing search engine homepage. Layout A, the current version, has a 45% user engagement rate, while Layout B, a new design, shows a 50% engagement rate. In a recent user study with 10,000 participants randomly assigned to experience either layout, determine if the observed difference in engagement rates is statistically significant.
How to Respond:
Clarify that the task involves comparing two proportions (engagement rates) to assess if the observed difference is statistically significant. Describe the use of a hypothesis test, like a two-proportion z-test or chi-square test, based on engagement rates and the number of users exposed to each layout.
Example:
“To evaluate the significance of the observed difference in engagement rates between Layouts A and B, a suitable approach would be a two-proportion z-test. The null hypothesis assumes no difference in engagement rates. The z-score indicates how many standard deviations away from the mean the observed difference is. Comparing this z-score to a critical value from the z-table at a chosen significance level, such as 0.05 for a 95% confidence level, allows us to make a decision. If the calculated z-score surpasses the critical value, we reject the null hypothesis, concluding that the difference in engagement rates is statistically significant.
It’s crucial to consider underlying assumptions, ensuring that samples are representative, independent, and external factors like marketing campaigns or seasonal effects did not influence engagement during the testing period.”
14. In a Microsoft interview, where understanding market trends and customer preferences is crucial for guiding product strategy, imagine having data on the sales of different Microsoft Surface models over the last quarter. The goal is to analyze trends and provide insights to the product team for potential areas of development or improvement in the next generation of devices.
How to Respond:
Outline a comprehensive approach to analyze sales data, covering trend analysis, customer segmentation, A/B testing, and correlation analysis.
Example:
“In analyzing the sales data for Microsoft Surface models over the last quarter, my approach would begin with a thorough trend analysis. This involves identifying patterns such as peak sales periods and recognizing models with consistently high or low sales. To gain deeper insights, I would segment the data based on key demographics, regional variations, and specific features of the Surface models to understand user preferences more precisely. Additionally, I would correlate sales trends with customer feedback to pinpoint areas for potential improvement. By employing techniques like A/B testing, we can further validate hypotheses and inform the product team on strategic decisions for the next generation of devices.”
15. In a Microsoft interview, where the ability to translate product changes into measurable outcomes is crucial for product refinement, imagine the scenario of introducing a new feature in Excel. The product team seeks to understand its impact on user productivity. The objective is to assess your approach to selecting relevant metrics and designing an analysis to evaluate the success of the feature.
How to Respond:
Highlight the importance of identifying pertinent productivity metrics and designing an analysis that compares these metrics before and after the feature’s introduction. Discuss the potential use of A/B testing or longitudinal studies to measure the feature’s impact. Define how you would establish success benchmarks and specify the time horizon considered.
Example:
“To gauge the success of the new feature in Excel, I would focus on metrics such as average task completion time, error rates in data processing tasks, and user engagement with the feature, including frequency and duration of use. Implementing A/B testing would be a valuable approach to analyze user engagement more comprehensively. Additionally, I would advocate for collecting qualitative feedback through user surveys and leveraging text mining techniques to analyze the responses. This holistic approach would provide insights into both quantitative and qualitative aspects of the feature’s impact. In defining success benchmarks, I would consider setting realistic goals aligned with the objectives of the new feature, and the time horizon for evaluation would depend on the nature of the feature and its expected impact on user productivity.”
16. In a Microsoft interview, where understanding user behavior is crucial for a Data Analyst, consider a case study where the Xbox team is analyzing user engagement data. They observe that on weekends, the average session length is longer than on weekdays, but this trend reverses in Asia, where average session length is longer on weekdays. The goal is to assess your critical thinking skills in explaining these regional variations.
How to Respond:
Discuss the influence of cultural factors on user behavior in different regions. Suggest potential hypotheses or additional data points that could be explored to understand the underlying reasons for these trends.
Example:
“To unravel the variations in user engagement data on weekends and weekdays across regions, it’s essential to consider cultural factors that might influence gaming habits differently. Exploring demographic differences, such as the age distribution of Xbox users in these regions, or the availability of alternative leisure activities, could shed light on these trends. Additionally, examining marketing strategies or regional promotions during the given period might provide further insights into the observed patterns. Understanding the unique cultural dynamics in each region is key to comprehending the nuances in user behaviour.”
17. In a Microsoft interview, where analyzing usage data to inform infrastructure decisions is critical, imagine the scenario where Microsoft is contemplating expanding server capacity for OneDrive due to significant usage spikes during specific hours. The objective is to evaluate your ability to analyze usage data and determine whether these spikes represent consistent patterns or isolated incidents.
How to Respond:
Emphasize the importance of analyzing historical data over a substantial period to identify usage patterns. Discuss the type of analysis you would employ in this scenario.
Example:
“To assess the nature of usage spikes on OneDrive, I would conduct a comprehensive time series analysis of the usage data. This analysis would span various time frames – hourly, daily, and weekly – over an extended period to discern whether the spikes follow consistent patterns or are sporadic occurrences. By examining historical data, we can gain insights into the regularity and predictability of these usage spikes. Additionally, I would consider external factors that might contribute to these spikes, such as marketing campaigns, new feature releases, or global events. This multifaceted approach would provide a holistic understanding of the usage patterns and guide informed decisions on whether expanding server capacity is a warranted solution.”
Behavioural Questions
“Why do you want to work for Microsoft?”
Expressing genuine passion for the company’s values is crucial. The goal is to assess your alignment with the company’s mission and values, especially for the Data Analyst role.
How to Respond:
Articulate why you chose Microsoft and the Data Analyst role, emphasizing how you are a suitable match. Maintain a positive and honest tone while highlighting the value you can bring to the organization.
Example:
“My desire to work for Microsoft stems from a profound admiration for its dedication to innovation, particularly in areas like cloud computing and AI. The company’s role in shaping the future of technology resonates with my professional aspirations. Moreover, Microsoft’s commitment to fostering diversity and inclusion aligns seamlessly with my personal values. I see myself contributing to Microsoft’s success through a combination of technical proficiency and a genuine passion for data-driven problem-solving. My collaborative approach, honed through diverse team experiences, positions me as a great fit for the company’s dynamic and forward-thinking culture.”
“Tell me about a time you failed.” In a Microsoft interview, where a collaborative culture values openness about mistakes and a commitment to continuous improvement. The objective is to assess your ability to reflect on and learn from professional mistakes.
How to Respond:
Utilize the STAR (Situation, Task, Action, Result) method to structure your response coherently. Be honest and reflective, choosing a real example of a professional error. Describe what happened, emphasize what you learned, and explain how this experience shaped your growth. Highlight your sense of responsibility and convey how this experience has influenced your approach to challenges and teamwork.
Example:
“In a previous role, I led a project implementing a new data visualization tool. My confidence in the tool’s capabilities led to advocating for its implementation without thorough testing in our existing environment. Unfortunately, once deployed, we encountered significant compatibility issues.
This experience underscored the critical importance of comprehensive testing and validation, particularly when integrating new technology into existing systems. The lesson learned from this failure has transformed me into a more cautious and collaborative professional. It emphasized the need to balance innovation with practical execution and significantly improved my approach to teamwork and project management. Now, I prioritize a meticulous testing phase in any implementation, ensuring that lessons from this failure guide my decision-making process.”
In a Microsoft interview, where collaboration and leadership are integral to success, you could be asked, “Could you describe a project or initiative where you played a pivotal role in a team?” The goal is to assess your ability to contribute effectively to collaborative projects in a data-driven and diverse environment.
How to Respond:
Apply the STAR (Situation, Task, Action, Result) method for a well-organized response. Draw from examples of past collaborative projects, emphasizing quantifiable impacts.
Example:
“In a notable project focused on optimizing predictive maintenance for a client, our team confronted substantial challenges, including data quality issues and a tight timeline. As the lead analyst, I took charge of leading the data preprocessing efforts and fostered close collaboration with domain experts and subject matter experts to fine-tune the predictive maintenance model. Through our concerted efforts, we successfully achieved a 25% reduction in unplanned downtime, translating into significant cost savings for the client. This experience underscored the importance of collaborative problem-solving, effective leadership, and the direct impact that well-executed data analytics can have on business outcomes.”
Tips to Prepare for a Data Analyst Interview at Microsoft
Enhance your understanding of the Company and Role
Conduct thorough research on Microsoft, including recent news, company values, and ongoing challenges. This knowledge will not only enable you to present yourself effectively but also help you assess if the company aligns with your aspirations. Gain insights into the specific team you are applying to and understand how they contribute to the company’s overarching goals. Explore Interview Query members’ experiences for valuable insider tips.
Sharpen Technical Skills
Develop proficiency in SQL, Python, and BI tools, and ensure a solid grasp of statistics, product sense, Excel, and metric development. Practice solving SQL problems that encompass window functions, complex joins, subqueries, lead and lag functions, among others. Leverage free resources for Data Analysts, such as Excel interview questions and data visualization question guides. Boost your confidence by working on projects that replicate real-world analytics challenges.
Prepare for Behavioural Interview Questions
Recognize the significance of soft skills like collaboration, effective communication, and flexibility, especially in Microsoft’s collaborative culture. Conduct mock interviews to refine your communication skills and ensure you are well-prepared for the interview process.
Ask Thoughtful Questions
Demonstrate your interest in the role and the company by having well-thought-out questions for your interviewer. This not only showcases your engagement but also provides valuable insights into the Microsoft work environment. For additional guidance on interview preparation as a Data Analyst, refer to our comprehensive guide.
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Amazon, renowned as a global e-commerce giant, stands out from traditional marketplaces due to its vast scale, featuring an extensive array of millions of products. In the United States, Amazon dominates over 50% of the online market, showcasing its substantial influence. Since its establishment in 1994, Amazon has consistently pursued its overarching objective of becoming the ultimate “one-stop-shop,” a mission facilitated significantly by its meticulous reliance on data. This data-centric approach holds significant relevance for individuals gearing up for Amazon Data Analyst interviews, highlighting the pivotal role of data in driving Amazon’s operational strategies.
Here are some of the finest Amazon Data Analyst Interview Questions.
7. Onsite: Round 4- behavioral questions, system design.
8. Onsite: Round 5 – human relations
HR Initial Screening
Are you open to relocation for this position?
What factors are motivating your decision to explore new opportunities?
How long have you been in your current role?
Can you provide a detailed overview of your professional journey as outlined in your resume?
What motivated you to seek employment at Amazon?
What specific aspects of the job description caught your attention and made you interested?
Could you share your salary expectations for this position?
What is your earliest availability to start in this role?
The initial stage of the interview process at Amazon involves a recruiter phone screen lasting 30-60 minutes. This call serves the dual purpose of evaluating whether you meet the fundamental qualifications for the job and assessing your compatibility with Amazon’s culture. The recruiter will delve into standard questions about your background, experience, and salary expectations. Beyond these aspects, candidates often overlook the significance of this call as an opportunity for the recruiter to gauge their alignment with Amazon’s Leadership Principles.
Understanding and articulating your comprehension of these principles becomes crucial during this stage. Moreover, the phone screen provides a chance to establish rapport with the recruiter, offering insights into the role and the reasons behind its recruitment. Successfully navigating this phase involves not only meeting the basic job requirements but also showcasing cultural fit and a genuine understanding of Amazon’s guiding principles.
Technical Screening: Round 1
In this round, candidates’ technical proficiency is assessed through inquiries spanning coding, machine learning, and algorithms. Amazon data analysts pose these questions to evaluate applicants’ readiness for the role by gauging their technical capabilities in these crucial areas.
Calculate the cumulative cost of each customer’s purchase and output their last names, locations, and IDs in alphabetical order based on last names.
Formulate a query to retrieve the total sales for each product in the month of February.
Define convex and non-convex cost functions and elucidate the distinctions between them.
Provide an explanation for the concept of overfitting in the context of data analysis or machine learning.
Share your perspective on whether adjusting the prime membership fee would have an impact on the market. If so, elaborate on how this change might influence the market dynamics.
Technical Screening: Round 2
In the second round of the technical screening, the focus shifts to the examination of Leadership Principles. Amazon places significant emphasis on this aspect, and the questions in this round revolve around assessing candidates’ alignment with and understanding of the company’s Leadership Principles.
What motivated your decision to join Amazon?
Describe a situation where you effectively managed a disagreement with your manager.
Can you share an instance when you faced challenges in meeting project deadlines and how you addressed it?
Onsite: Round 1
In this stage of the interview process, the emphasis is on Amazon’s Leadership Principles, as they play a crucial role in enhancing customer satisfaction. Anticipate broad questions centered on your personal experiences and professional background during this round. Amazon’s focus on leadership principles stems from their recognition of the profound impact it has on elevating customer satisfaction levels.
Detail a project you managed, highlighting your approaches to ensure timely completion.
Discuss a challenge you encountered while working on a project and how you addressed it.
Can you elaborate on your key strengths and areas you are actively working on for improvement?
Onsite: Round 2
In the second round, questions will continue to center around leadership principles, but the expectations are elevated. Additionally, there will be a focus on statistical knowledge, recognizing its integral role in data analysis, and assessing your proficiency to determine your suitability for the position.
Provide insights into specific projects listed on your resume, discussing your roles and contributions.
Among Amazon’s leadership principles, which resonates with you the most? Conversely, which one do you find most challenging and why?
Share an experience where you devised a solution for a particularly intricate situation.
In scenarios where there’s a high ratio of negative to positive data, how would you effectively address the imbalance?
For an engineer, elucidate the significance of the p-value.
When faced with collinearity, what steps would you take to address the situation?
Onsite: Round 3
During the third round, expect inquiries pertaining to machine learning, SQL proficiency, and technical skills. These skills, particularly SQL and machine learning, are highly sought-after in the field of data analysis.
Provide an explanation of a machine learning model and elucidate its relevance in practical applications.
How do you communicate technical concepts such as R-square to non-technical colleagues? Share your approach.
Talk about a specific machine learning technique that captures your interest and explain why.
Contrast the characteristics of a queue and a stack.
In your perspective, are there any distinctions between an array and a linked list? If so, what are they?
Some more Technical Questions-
Question: Explain the purpose of a DISTINCT clause in SQL.
Answer: The DISTINCT clause is utilized to retrieve only unique (distinct) values from a query result.
Question: Compare and contrast UNION and UNION ALL in SQL.
Answer: The UNION operator extracts distinct rows from the queries specified, whereas UNION ALL retrieves all rows, including duplicates, from both queries.
Question: How do Window Functions operate in SQL?
Answer: Window Functions perform calculations at the row level with a set of related values. Unlike aggregate functions like SUM(), window functions don’t consolidate the rows into a single value; instead, they return output for each row.
Question: Differentiate between a primary key and a foreign key in a database.
Answer: A primary key is a column or set of columns in a table that uniquely identifies rows in that table. On the other hand, a foreign key is a column or set of columns in a table whose values correspond to the primary key values in another table.
Question: Explain the distinctions between the SQL functions LEAD and LAG.
Answer: LEAD provides values from rows occurring after the specified row, while LAG provides values from rows occurring before the specified row.
Onsite: Round 4
In the fourth round, anticipate behavioral inquiries alongside questions related to system design. A comprehensive grasp of the end-to-end process of creating and launching systems from inception to completion is essential for addressing these questions effectively.
Share an experience from your professional background where you collaborated with non-technical colleagues.
Describe a situation when your perspective diverged from that of your colleagues and how you navigated through it.
Provide an explanation of SCD (Slowly Changing Dimension).
Outline the design of a Data Warehouse (DWH) tailored for the media team to compile statistics about their services.
Onsite: Round 5
In this segment, the focus lies on interpersonal skills, where the evaluation centers around your behavioral aptitude. Amazon places a significant emphasis on the human relations capabilities of their data analysts, recognizing the importance of adeptly servicing individuals from diverse backgrounds and locations.
Describe an instance where you provided support to a team member.
Outline your professional aspirations for the upcoming five years.
What factors influenced your decision to join Amazon?
Do you have any specific inquiries you would like to pose to us?
The Data Monk services
We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now
YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions Link –The Data Monk Youtube Channel
Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study Link –The Data Monk website
E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out Link –The Data E-shop Page
Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms Link –The Data Monk Instagram page
