Adobe Analytics Interview Questions – SQL
Adobe Analytics Interview Questions
What was the project for?
A. The project was to do sentiment analysis on the survey data filled by online customers.
2. What algorithms/methods did you try?
A. We tried multiple algorithms, starting from TF-IDF, Part-Of-Speech tagging, n-gram, Lemmatization, Stemming, Tokenization, Latent Semantic Indexing, Sentiment Analysis.
3. What all methods do you need to perform in order to convert a keyword into its base form(Normalization)?
A. Lemmatization and Stemming
4. What is N-gram?
A. N-grams are simply all combinations of adjacent words or letters of length n that you can find in your text file.
For example
This is a sentence
N-grams = This is, is a, a sentence
5. What is the use of TF-IDF?
A. TF-IDF stands for Term Frequency and Inverse Document Frequency. TF-IDF is numerical statistics that help to understand the importance of a particular word in a document. Term frequency gets you the number of times a particular word has occurred in a document and Inverse Document Frequency gets you the importance of the words. It helps out in filtering out the most common words like a, an, the, was, etc.. So, you get only the important terms.
6. What is Lemmatization?
Lemmatization takes into account the morphological analysis of the word. It converts a word into its pure root form by looking into the morphological information studies – Third-person, singular number, present tense of verb study
Lemma – study
studying – Gerund of the verb study
Lemma – study
As you can see, both the words studies and studying has been narrowed down to the lemma study.
7. Explain the complete flow of your NLP project
A. The brief of the process is given below with some coding examples:-
Step 1 – Get the text dataset
Step 2 – Tokenize the text using get_text() in Python
Step 3 – Split the text using
tokens = [t for t in text.split()]
Step 4 – Get the count of the word frequency using the NLTK package in Python
freq = nltk.FreqDist(tokens)
Step 5 – Remove stop words. Code below
for token in tokens:
if token in stopwords.words(‘english’):
clean_tokens.remove(token) Step 6 – Tokenize non-
Adobe Analytics Interview Questions
What was the project for?
A. The project was to do sentiment analysis on the survey data filled by online customers.
2. What algorithms/methods did you try?
A. We tried multiple algorithms, starting from TF-IDF, Part-Of-Speech tagging, n-gram, Lemmatization, Stemming, Tokenization, Latent Semantic Indexing, Sentiment Analysis.
3. What all methods do you need to perform in order to convert a keyword into its base form(Normalization)?
A. Lemmatization and Stemming
4. What is N-gram?
A. N-grams are simply all combinations of adjacent words or letters of length n that you can find in your text file.
For example
This is a sentence
N-grams = This is, is a, a sentence
5. What is the use of TF-IDF?
A. TF-IDF stands for Term Frequency and Inverse Document Frequency. TF-IDF is numerical statistics that help to understand the importance of a particular word in a document. Term frequency gets you the number of times a particular word has occurred in a document and Inverse Document Frequency gets you the importance of the words. It helps out in filtering out the most common words like a, an, the, was, etc.. So, you get only the important terms.
6. What is Lemmatization?
Lemmatization takes into account the morphological analysis of the word. It converts a word into its pure root form by looking into the morphological information studies – Third-person, singular number, present tense of verb study
Lemma – study
studying – Gerund of the verb study
Lemma – study
As you can see, both the words studies and studying has been narrowed down to the lemma study.
7. Explain the complete flow of your NLP project
A. The brief of the process is given below with some coding examples:-
Step 1 – Get the text dataset
Step 2 – Tokenize the text using get_text() in Python
Step 3 – Split the text using
tokens = [t for t in text.split()]
Step 4 – Get the count of the word frequency using the NLTK package in Python
freq = nltk.FreqDist(tokens)
Step 5 – Remove stop words. Code below
for token in tokens:
if token in stopwords.words(‘english’):
clean_tokens.remove(token) Step 6 – Tokenize non-
What was the project for?
A. The project was to do sentiment analysis on the survey data filled by online customers.
2. What algorithms/methods did you try?
A. We tried multiple algorithms, starting from TF-IDF, Part-Of-Speech tagging, n-gram, Lemmatization, Stemming, Tokenization, Latent Semantic Indexing, Sentiment Analysis.
3. What all methods do you need to perform in order to convert a keyword into its base form(Normalization)?
A. Lemmatization and Stemming
4. What is N-gram?
A. N-grams are simply all combinations of adjacent words or letters of length n that you can find in your text file.
For example
This is a sentence
N-grams = This is, is a, a sentence
5. What is the use of TF-IDF?
A. TF-IDF stands for Term Frequency and Inverse Document Frequency. TF-IDF is numerical statistics that help to understand the importance of a particular word in a document. Term frequency gets you the number of times a particular word has occurred in a document and Inverse Document Frequency gets you the importance of the words. It helps out in filtering out the most common words like a, an, the, was, etc.. So, you get only the important terms.
6. What is Lemmatization?
Lemmatization takes into account the morphological analysis of the word. It converts a word into its pure root form by looking into the morphological information studies – Third-person, singular number, present tense of verb study
Lemma – study
studying – Gerund of the verb study
Lemma – study
As you can see, both the words studies and studying has been narrowed down to the lemma study.
7. Explain the complete flow of your NLP project
A. The brief of the process is given below with some coding examples:-
Step 1 – Get the text dataset
Step 2 – Tokenize the text using get_text() in Python
Step 3 – Split the text using
tokens = [t for t in text.split()]
Step 4 – Get the count of the word frequency using the NLTK package in Python
freq = nltk.FreqDist(tokens)
Step 5 – Remove stop words. Code below
for token in tokens:
if token in stopwords.words(‘english’):
clean_tokens.remove(token) Step 6 – Tokenize non-
What was the project for?
A. The project was to do sentiment analysis on the survey data filled by online customers.
2. What algorithms/methods did you try?
A. We tried multiple algorithms, starting from TF-IDF, Part-Of-Speech tagging, n-gram, Lemmatization, Stemming, Tokenization, Latent Semantic Indexing, Sentiment Analysis.
3. What all methods do you need to perform in order to convert a keyword into its base form(Normalization)?
A. Lemmatization and Stemming
4. What is N-gram?
A. N-grams are simply all combinations of adjacent words or letters of length n that you can find in your text file.
For example
This is a sentence
N-grams = This is, is a, a sentence
5. What is the use of TF-IDF?
A. TF-IDF stands for Term Frequency and Inverse Document Frequency. TF-IDF is numerical statistics that help to understand the importance of a particular word in a document. Term frequency gets you the number of times a particular word has occurred in a document and Inverse Document Frequency gets you the importance of the words. It helps out in filtering out the most common words like a, an, the, was, etc.. So, you get only the important terms.
6. What is Lemmatization?
Lemmatization takes into account the morphological analysis of the word. It converts a word into its pure root form by looking into the morphological information studies – Third-person, singular number, present tense of verb study
Lemma – study
studying – Gerund of the verb study
Lemma – study
As you can see, both the words studies and studying has been narrowed down to the lemma study.
7. Explain the complete flow of your NLP project
A. The brief of the process is given below with some coding examples:-
Step 1 – Get the text dataset
Step 2 – Tokenize the text using get_text() in Python
Step 3 – Split the text using
tokens = [t for t in text.split()]
Step 4 – Get the count of the word frequency using the NLTK package in Python
freq = nltk.FreqDist(tokens)
Step 5 – Remove stop words. Code below
for token in tokens:
if token in stopwords.words(‘english’):
clean_tokens.remove(token) Step 6 – Tokenize non-
1200+ Interview Questions on all the important Machine Learning algorithms (including complete Python code) Ada Boost, CNN, ANN, Forecasting (ARIMA, SARIMA, ARIMAX), Clustering, LSTM, SVM, Linear Regression, Logistic Regression, Sentiment Analysis, NLP, K-Mean – https://thedatamonk.com/product/machine-learning-interview-questions/
Set C – [0-7 Years of Experience]
2000+ interview questions that include 100 questions each on 12 most asked Machine Learning Algorithms, Python, Numpy and Pandas – 300 Interview Questions, Pandas,PCA,AWS,Data Preprocessing,Case Studies, and many more
https://thedatamonk.com/product/the-data-monk-e-book-bundle/
If you wish to get all the study material and topics to cover for an interview at one place, then you can subscribe to our channel. We have covered the complete syllabus of
Get all the youtube videos playlist on our youtube Channel – The Data Monk
