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2103 Curated Machine Learning, Data Science, AI & LLMs Interview Questions
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Top 28 Feature Engineering Interview Questions

Entry Junior Mid Senior Expert
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Learning Progress:
Feature Engineering Theoretical Questions
Q1:   

What are some common Machine Learning problems that Unsupervised Learning can help with?

  Related To: Unsupervised Learning
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Q2:   

What's the difference between Feature Engineering vs. Feature Selection?

  Related To: Data Processing
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Q3:   

What are some recommended choices for Imputation Values?

  Related To: ML Design Patterns, Data Processing
Add to PDF   Junior 
Q4:   

Can we use PCA for feature selection?

  Related To: PCA
Add to PDF   Junior 
Q5:   

What advantages does Deep Learning have over Machine Learning?

  Related To: Deep Learning
Add to PDF   Junior 
Q6:   

What's the difference between Forward Feature Selection and Backward Feature Selection?

  
Add to PDF   Junior 
Q7:   

How do you use the F-test to select features?

  
Add to PDF   Junior 
Q8:   

Name some benefits of Feature Selection

  Related To: Data Processing
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Q9:   

What are the basic objects that a ML pipeline should contain?

  Related To: Scikit-Learn
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Q10:   

Explain One-Hot Encoding and Label Encoding. Does the dimensionality of the dataset increase or decrease after applying these techniques?

  Related To: Dimensionality Reduction
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Q11:   

How would you decide on the importance of variables for the Multivariate Regression model?

  Related To: Linear Regression
 Add to PDF   Mid 
Q12:   

What methods to perform Feature Engineering from text data do you know?

  Related To: NLP, LLMOps
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Q13:   

How do you perform Principal Component Analysis (PCA)?

  Related To: PCA
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Q14:   

How do you transform a Skewed distribution into a Normal distribution?

  Related To: Statistics
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Q15:   

How do you perform feature selection with Categorical Data?

  
 Add to PDF   Mid 
Q16:   

What Feature Selection methods do you know?

  
 Add to PDF   Mid 
Q17:   

How do you perform End of Tail Imputation?

  
 Add to PDF   Mid 
Q18:   

When would you remove Correlated Variables?

  Related To: Data Processing
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Q19:   

How to choose the features for a Neural Network?

  Related To: Data Processing, Neural Networks
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Q20:   

How has Translation of words improved from the Traditional methods?

  Related To: NLP, Neural Networks
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Q21:   

How would you improve the performance of Random Forest?

  Related To: Random Forest
 Add to PDF   Mid 
Q22:   

Why would you use Permutation Feature Importance and how does this algorithm work?

  
 Add to PDF   Senior 
Q23:   

How does the Recursive Feature Elimination (RFE) work?

  
 Add to PDF   Senior 
Q24:   

What's the difference between Principal Component Analysis and Independent Component Analysis?

  Related To: PCA
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Q25:   

Explain the Stepwise Regression technique

  Related To: Linear Regression
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Q26:   

How to perform Feature Engineering on Unknown features?

  Related To: Data Processing
 Add to PDF   Expert 
Q27:   

When would you use Fine-Tuning vs Feature Extraction in Transfer Learning?

  Related To: ML Design Patterns, Data Processing
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Feature Engineering Practical Challenges
Q1:   

If we have a date column in our dataset, then how will you perform Feature Engineering using Python?

  Related To: Pandas, Dimensionality Reduction
 Add to PDF   Junior 
 
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