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

Why It Matters

A thorough grasp of these topics equips candidates to clear technical screens and adds immediate value to firms building next‑generation generative‑AI systems.

Key Takeaways

• Master Python fundamentals before tackling advanced AI concepts.
• Understand probability basics like Monty Hall to demonstrate Bayesian thinking.
• Distinguish between Poisson and binomial for appropriate event modeling.
• Grasp CLT, stratified sampling, and LLN for robust statistical inference.
• Know RAG and agentic AI trends shaping senior-level interview questions.

Summary

The video serves as an all‑in‑one interview preparation guide for data‑science and generative-AI roles in 2026, walking viewers through Python fundamentals, core statistics, machine‑learning concepts, deep‑learning foundations, Retrieval‑Augmented Generation (RAG), and emerging agentic‑AI topics.

It systematically reviews Python basics—data types, mutability, list operations, loops, exception handling, decorators, and OOP inheritance—then shifts to statistical reasoning, illustrating Bayesian updates with the Monty Hall paradox, contrasting Poisson and binomial distributions, and emphasizing the Central Limit Theorem, stratified sampling, and the law of large numbers as essential tools for robust modeling.

The presenter highlights concrete examples: using ‘+’ versus ‘extend’ for list concatenation, the super() function for multiple inheritance, and the Poisson limit theorem that links large‑N binomial trials to Poisson outcomes. A memorable line stresses that “switching doors in Monty Hall raises your win probability to two‑thirds,” underscoring Bayesian intuition.

By mastering these concepts, candidates can answer both classic and cutting‑edge interview questions, demonstrate quantitative rigor, and signal readiness for senior positions where RAG pipelines and agentic‑AI architectures dominate product roadmaps.

Original Description

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) Error

30:03 - Q9: Bagging & The No Free Lunch Theorem

30:37 - Q10: Hard Voting vs. Soft Voting

31:10 - Q11: Choice of Weak Learners in Boosting

31:48 - Q12: Feature Selection vs. Feature Extraction

32:31 - Q13: How Cross-Validation Improves Performance

33:03 - Q14: Feature Scaling vs. Normalization

33:42 - Q15: Pruning (During vs. After Training)

35:00 - Q16: Grid Search CV

35:36 - Q17: False Positives vs. False Negatives

36:25 - Q18: PCA for Feature Selection

37:01 - Q19: Dealing with High Bias and Low Variance

37:48 - Q20: Interpretation of ROC-AUC Curve

Deep Learning & Neural Networks

38:49 - Q1: Traditional ML vs. Deep Learning

40:59 - Q2: ANN Structure & Propagation

42:36 - Q3: The Mistake of Zero Weight Initialization

43:45 - Q4: Activation Functions & The Dead ReLU Problem

45:22 - Q5: Overfitting: Detection & Regularization

46:48 - Q6: Vanishing vs. Exploding Gradients

48:17 - Q7: Receptive Fields in CNNs

49:45 - Q8: Purpose of 1x1 Convolutions

51:02 - Q9: Sigmoid vs. Tanh in LSTMs

52:34 - Q10: Teacher Forcing & Exposure Bias

53:48 - Q11: LSTM Limitations & The Attention Fix

55:03 - Q12: Scaled Dot-Product Attention (Scaling factor)

56:01 - Q13: Multi-Head vs. Single-Head Attention

57:04 - Q14: Positional Encoding in Transformers

58:17 - Q15: Pre-Layer vs. Post-Layer Normalization

RAG/Retrieval Augmented Generation

59:56 - Q1: Importance of RAG in Modern LLM Systems

1:00:48 - Q2: Retriever vs. Generator Components

1:01:41 - Q3: Sparse vs. Dense vs. Hybrid Retrieval

1:02:54 - Q4: Indexing Strategies: HNSW vs. IVF

1:03:53 - Q5: Parent Document vs. Sentence Window Retrieval

1:04:56 - Q6: Solving the "Lost in the Middle" Problem

1:06:02 - Q7: Improving Retrieval with HyDE

1:07:02 - Q8: Pre-Retrieval Query Reformulation

1:08:00 - Q9: Standard vs. Corrective (CRAG) vs. Self-RAG

1:08:50 - Q10: Designing Agentic RAG Systems

1:09:36 - Q11: Graph RAG vs. Traditional Vector RAG

1:10:25 - Q12: Do Long Context Models make RAG obsolete?

1:11:11 - Q13: RAG Evaluation Frameworks (RAGAS)

1:12:00 - Q14: Optimizing for Accuracy and Latency

1:12:56 - Q15: Red Flags in RAG System Design

Agentic AI & Production Concerns

1:13:01 - Q1: Agentic AI vs. Traditional Generative AI

1:15:41 - Q2: Andrew Ng's 4 Agentic Design Patterns

1:17:03 - Q3: Understanding the ReAct Framework

1:18:27 - Q4: System Prompts vs. User Prompts

1:19:58 - Q5: Implementation of Reflection Loops

1:21:10 - Q6: Tool Use & Function Calling Mechanics

1:22:20 - Q7: Engineering Agentic Memory Systems

1:23:44 - Q8: Multi-Agent Orchestration & Routing

1:25:01 - Q9: Frameworks: LangChain vs. LangGraph vs. AutoGen

1:26:36 - Q10: Evals in Agentic Systems (LLM as a Judge)

1:27:54 - Q11: Tracing, Spans, and Observability

1:28:57 - Q12: Text vs. Functional Hallucinations

1:29:51 - Q13: Handling Non-Deterministic Behavior

1:30:51 - Q14: Risk of Infinite Loops & Agent Sprawl

1:31:54 - Q15: Security, Deployment & Privilege Escalation

1:33:44 - Conclusion