The top 50 most frequently asked questions across all topics — curated from 500+ real interview experiences at FAANG, startups, and Indian IT companies.
Asked in 80%+ of ML interviews. Tests fundamental understanding of model performance. Expected at: Google, Amazon, Meta, any ML role.
Bias = underfitting (too simple). Variance = overfitting (too complex). Goal = find the sweet spot. Total Error = Bias² + Variance + Noise.
💡 Deep dive: ML Fundamentals Q1
Every Python interview starts here. Tests understanding of mutability, memory, and use cases. Expected at: All companies for Python roles.
Lists are mutable (changeable), tuples are immutable (fixed). Tuples are faster, hashable (usable as dict keys), and signal "this data shouldn't change."
💡 Deep dive: Python Basics Q3
Foundation of all modern AI (GPT, BERT, Stable Diffusion). Asked at every AI company, and increasingly at product companies. Must-know for 2024-2025.
Self-attention mechanism processes all tokens in parallel (no sequential dependency). Key components: Multi-Head Attention, Positional Encoding, Feed-Forward layers, Layer Norm + Residuals.
💡 Deep dive: Deep Learning Q102
Classic SQL interview question. Tests understanding of subqueries, window functions, and LIMIT/OFFSET. Expected at: All data roles.
-- Method 1: Subquery
SELECT MAX(salary) FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);
-- Method 2: DENSE_RANK
SELECT salary FROM (
SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) as rnk
FROM employees
) t WHERE rnk = 2;💡 Deep dive: SQL Queries Q2
Questions 5-50 continue covering the most asked topics.
Includes: OOP pillars, GIL, Decorators, Random Forest vs XGBoost, CNN architectures, RAG pipelines, ACID properties, Tell me about yourself, and 40+ more frequently asked questions from real interviews.
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