Company: Oracle
Designation: Data Analyst
Year of Experience Required: 0 to 4 years
Technical Expertise: SQL, Python/R, Statistics, Machine Learning, Case Studies
Salary Range: 15 LPA – 30 LPA

Oracle Corporation, headquartered in Redwood Shores, California, is a global leader in database software, cloud-engineered systems, and enterprise software products. Known for its innovative solutions, Oracle is a trusted name in the tech industry. If you’re preparing for a Data Analyst role at Oracle, here’s a detailed breakdown of their interview process and the types of questions you can expect.

Oracle Data Analyst Interview Questions

Interview Process

The Oracle Data Analyst interview process typically consists of 5 rounds, each designed to evaluate different aspects of your technical and analytical skills:

Round 1 – Telephonic Screening

Focus: Basic understanding of Data Science concepts, SQL, and Python/R.
Format: You’ll be asked to explain your projects and solve a few coding or SQL problems.

Round 2 – Walk-in/Face-to-Face Technical Round

Focus: Advanced SQL, coding, and problem-solving.
Format: You’ll solve problems on a whiteboard or shared document.

Round 3 – Project Analysis

Focus: Deep dive into your past projects.
Format: You’ll be asked to explain your approach, tools used, and the impact of your work.

Round 4 – Case Studies

Focus: Business problem-solving and data-driven decision-making.
Format: You’ll be given a real-world scenario and asked to propose solutions.

Round 5 – Hiring Manager Round

Focus: Cultural fit, communication skills, and long-term career goals.
Format: Behavioral questions and high-level discussions about your experience.

Difficulty of Questions

SQL – 8/10

1) How do you find customers who have ordered the same product more than 5 times?

SELECT customer_id, product_id, COUNT(*) AS order_count
FROM orders
GROUP BY customer_id, product_id
HAVING order_count > 5;

2) How can you retrieve the most expensive product in each category?

SELECT category_id, product_id, price
FROM products
WHERE price IN (
SELECT MAX(price)
FROM products
GROUP BY category_id
);

3) How do you find orders placed on Saturdays or Sundays?

SELECT *
FROM orders
WHERE DAYOFWEEK(order_date) IN (1, 7);

4) How can you list employees earning more than the average salary?

SELECT name, salary
FROM employees
WHERE salary > (SELECT AVG(salary) FROM employees);

5) How do you find the first order of every customer?

SELECT customer_id, MIN(order_date) AS first_order_date
FROM orders
GROUP BY customer_id;

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R/Python – 7/10

1) Write a Python function to calculate the moving average of a column in a Pandas DataFrame with a given window size.

2) Write a Python function to find unique values and their counts in a Pandas DataFrame column.

3) Write a Python function to fill missing values (NaN) in a Pandas DataFrame column with the mean of the column.

4) Write a Python function to convert a dictionary into a Pandas DataFrame.

5) Write a Python function to find the median of a list of numbers without using NumPy.

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Statistics/ML

1) You are about to send a million emails. How do you optimize delivery? How do you optimize the response?

To optimize email delivery:

• Use a reputable email provider to avoid spam filters (e.g., Amazon SES, SendGrid).
• Authenticate emails using SPF, DKIM, and DMARC to ensure credibility.
• Throttle email sending to avoid triggering spam filters by gradually increasing the email volume.
• Clean the email list by removing invalid, inactive, or unverified emails.

To optimize response rate:

• Personalize emails by addressing the recipient’s name and preferences.
• Optimize subject lines to be engaging and avoid spam-triggering words.
• A/B test email content to analyze which version gets better responses.
• Send emails at optimal times based on the audience’s activity patterns.
• Use clear CTAs (Call-to-Actions) to encourage engagement.

2) What is one way that you would handle an imbalanced dataset that’s being used for prediction?

To handle an imbalanced dataset, one common approach is resampling:

• Oversampling the minority class using techniques like SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic examples.
• Undersampling the majority class to balance the dataset by removing redundant samples.

Other approaches include:

• Using different evaluation metrics like F1-score, Precision-Recall, and AUC-ROC instead of Accuracy.
• Adjusting model weights to give more importance to the minority class.
• Using anomaly detection techniques when dealing with rare event predictions.

3) What are the benefits of a single decision tree compared to more complex models?

A single decision tree has several advantages over complex models like Random Forest or Gradient Boosting:

• Interpretability – Easy to understand and visualize.
• Low computational cost – Faster training and inference time.
• No need for feature scaling – Works well with categorical and numerical data.
• Handles missing values – Can split data even with missing attributes.
• Less prone to overfitting on small datasets compared to deep learning models.

However, decision trees are prone to overfitting on large datasets, making ensemble methods like Random Forest or Boosting more effective.

4) Can we formulate the search problem as a classification problem? How?

Yes, a search problem can be formulated as a classification problem by:

• Defining search relevance as a binary or multi-class label (e.g., relevant vs. non-relevant).
• Using past user behavior (clicks, dwell time, purchases) as labeled data.
• Training a machine learning model to classify search results based on user preferences.

For example:

• Google Search may classify whether a webpage is relevant or irrelevant to a query.
• E-commerce platforms rank products using a classification model based on user history.

This helps improve personalized recommendations and ranking accuracy.

5) Is it easy to parallelize training of a random forest model? How can we do it?

Yes, Random Forest is highly parallelizable because:

• Each tree in the forest is trained independently on a different subset of data.
• Each tree uses a random subset of features, reducing dependency between trees.

To parallelize Random Forest training:

• In Python (Scikit-learn), set n_jobs=-1 to use all CPU cores.
• Use distributed computing with tools like Spark’s MLlib or Dask.
• GPU-based implementations (e.g., NVIDIA RAPIDS) accelerate training.

This parallelization makes Random Forest scalable for large datasets.

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Case Study

Problem Statement:

Oracle wants to analyze customer churn for its cloud services. The company is seeing a decline in customer retention, and the leadership wants to identify factors leading to churn and propose strategies to improve customer loyalty.

Your task as a Data Analyst is to examine historical customer data, identify trends, and recommend actionable insights to reduce churn.

Dataset Overview:

You have access to a dataset containing customer subscription details and engagement metrics. The dataset includes:

• Customer_ID – Unique identifier for each customer
• Subscription_Type – Plan type (Basic, Standard, Premium)
• Contract_Length – Duration of the subscription (Monthly, Annual)
• Monthly_Spend – Amount spent on Oracle services per month
• Login_Frequency – Number of logins per month
• Support_Tickets_Raised – Number of customer support requests
• Service_Uptime (%) – Availability of Oracle cloud services for the customer
• Feature_Usage (%) – Percentage of product features used by the customer
• Customer_Satisfaction_Score – Rating from customer feedback surveys
• Churn_Status – 1 if the customer churned, 0 if they are retained

Key Questions to Answer:

1. What factors contribute to customer churn?

• Do customers with lower feature usage have a higher churn rate?
• Does poor service uptime lead to dissatisfaction and increased churn?
• How does support ticket frequency impact retention?

2. How can Oracle improve customer retention?

• Should Oracle personalize customer engagement based on usage behavior?
• Can Oracle use predictive analytics to detect early signs of churn?
• Should Oracle offer loyalty discounts or customized renewal plans?

3. What strategic actions can reduce churn while maximizing revenue?

• Should Oracle focus more on premium subscribers or optimize lower-tier plans?
• How can Oracle proactively support at-risk customers?
• Can Oracle improve customer education and onboarding?

Key Insights & Business Recommendations

1. Identifying Customer Churn Risk Factors

• Low Feature Usage Correlates with High Churn: Customers who use less than 30% of the product’s features are more likely to cancel their subscriptions. Oracle should focus on increasing feature adoption.
• Frequent Support Tickets Indicate Dissatisfaction: Customers who raise multiple support tickets are at higher risk of churn. Improving customer service response times can enhance retention.
• Service Downtime Negatively Impacts Retention: Customers experiencing uptime below 98% show a higher tendency to churn. Oracle must prioritize cloud infrastructure reliability.

2. Improving Oracle’s Customer Retention Strategy

• AI-Based Churn Prediction Models: By analyzing login frequency, support ticket volume, and feature usage, Oracle can identify early signs of customer dissatisfaction and take proactive action.
• Customer Engagement Campaigns: Personalized product tutorials, onboarding webinars, and feature recommendations can help increase product adoption and retention.
• Tiered Loyalty Programs: Offering discounted renewals for long-term customers can improve retention, especially for monthly subscribers who are more likely to churn.

3. Strategic Actions for Reducing Churn & Maximizing Revenue

• Upselling High-Value Features: Encouraging Basic plan users to upgrade by showcasing the benefits of premium features can increase revenue and retention.
• Dedicated Account Managers for High-Risk Customers: Assigning customer success managers to at-risk users can improve retention by providing personalized support.
• Improving Service Reliability: Investing in cloud infrastructure upgrades can reduce service downtime and prevent churn caused by technical issues.

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