Ultimate Generative AI Interview Guide 2026 | Python, ML, RAG & Agentic AI Interview Questions

Analytics Vidhya · Advanced ·📐 ML Fundamentals ·4h ago
GenAI Interview Questions & Answers- Python Concepts 0:59 - Q1: Basic Data Types in Python 1:36 - Q2: Lists vs. Tuples (Mutability) 2:16 - Q3: Concatenating Lists (Operator vs. Method) 2:51 - Q4: For Loop vs. While Loop 3:23 - Q5: How to Floor a Number 3:45 - Q6: Single Slash (/) vs. Double Slash (//) 4:05 - Q7: Passing Functions as Arguments 4:21 - Q8: Lambda Function 4:44 - Q9: List Comprehension Examples 5:02 - Q10: Understanding *args and **kwargs 5:17 - Q11: Set vs. Dictionary 5:38 - Q12: The Purpose of Docstrings 5:55 - Q13: Exception Handling (Try-Except-Finally) 6:16 - Q14: Shallow Copy vs. Deep Copy 6:37 - Q15: What is a Decorator? 7:01 - Q16: Range vs. Xrange 7:26 - Q17: Inheritance Fundamentals 7:50 - Q18: Supported Types of Inheritance 8:29 - Q19: Method Overriding & Polymorphism 8:52 - Q20: Use of the Super() Function Statistics & Probability 9:22 - Q1: Bayesian Inference & Monty Hall Paradox 10:38 - Q2: Poisson vs. Binomial Distribution 11:55 - Q3: Central Limit Theorem (CLT) Significance 13:00 - Q4: Stratified Sampling vs. SRS 14:14 - Q5: Law of Large Numbers vs. Gambler's Fallacy 15:01 - Q6: P-Values & NHST Framework 16:08 - Q7: Type I vs. Type II Errors 17:05 - Q8: Confidence vs. Prediction Intervals 17:55 - Q9: Determining Sample Size for AB Testing 18:41 - Q10: Parametric vs. Non-Parametric Testing 19:30 - Q11: The Bias-Variance Trade-off 20:17 - Q12: L1 vs. L2 Regularization (Lasso vs. Ridge) 21:10 - Q13: Simpson’s Paradox 22:05 - Q14: Berkson's Paradox (Selection Bias) 23:02 - Q15: Imputation Theory for Missing Data Machine Learning 24:55 - Q1: Why use Harmonic Mean for F1 Score? 25:28 - Q2: Purpose of Activation Functions 26:03 - Q3: Random Forest vs. Logistic Regression (Unscaled Data) 26:44 - Q4: Precision vs. Recall in Medical Diagnosis 27:27 - Q5: Impact of Skewness on Model Performance 28:25 - Q6: Lasso (L1) vs. Ridge (L2) Regularization 29:02 - Q7: Bayesian Optimization vs. Grid Search 29:30 - Q8: Significance of Out-of-Bag (OOB) Erro
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Chapters (43)

0:59 Q1: Basic Data Types in Python
1:36 Q2: Lists vs. Tuples (Mutability)
2:16 Q3: Concatenating Lists (Operator vs. Method)
2:51 Q4: For Loop vs. While Loop
3:23 Q5: How to Floor a Number
3:45 Q6: Single Slash (/) vs. Double Slash (//)
4:05 Q7: Passing Functions as Arguments
4:21 Q8: Lambda Function
4:44 Q9: List Comprehension Examples
5:02 Q10: Understanding *args and **kwargs
5:17 Q11: Set vs. Dictionary
5:38 Q12: The Purpose of Docstrings
5:55 Q13: Exception Handling (Try-Except-Finally)
6:16 Q14: Shallow Copy vs. Deep Copy
6:37 Q15: What is a Decorator?
7:01 Q16: Range vs. Xrange
7:26 Q17: Inheritance Fundamentals
7:50 Q18: Supported Types of Inheritance
8:29 Q19: Method Overriding & Polymorphism
8:52 Q20: Use of the Super() Function
9:22 Q1: Bayesian Inference & Monty Hall Paradox
10:38 Q2: Poisson vs. Binomial Distribution
11:55 Q3: Central Limit Theorem (CLT) Significance
13:00 Q4: Stratified Sampling vs. SRS
14:14 Q5: Law of Large Numbers vs. Gambler's Fallacy
15:01 Q6: P-Values & NHST Framework
16:08 Q7: Type I vs. Type II Errors
17:05 Q8: Confidence vs. Prediction Intervals
17:55 Q9: Determining Sample Size for AB Testing
18:41 Q10: Parametric vs. Non-Parametric Testing
19:30 Q11: The Bias-Variance Trade-off
20:17 Q12: L1 vs. L2 Regularization (Lasso vs. Ridge)
21:10 Q13: Simpson’s Paradox
22:05 Q14: Berkson's Paradox (Selection Bias)
23:02 Q15: Imputation Theory for Missing Data
24:55 Q1: Why use Harmonic Mean for F1 Score?
25:28 Q2: Purpose of Activation Functions
26:03 Q3: Random Forest vs. Logistic Regression (Unscaled Data)
26:44 Q4: Precision vs. Recall in Medical Diagnosis
27:27 Q5: Impact of Skewness on Model Performance
28:25 Q6: Lasso (L1) vs. Ridge (L2) Regularization
29:02 Q7: Bayesian Optimization vs. Grid Search
29:30 Q8: Significance of Out-of-Bag (OOB) Erro
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