21 Python Interview Questions Asked at FAANG for AI/ML Roles in 2026 (Real Questions) — FREE PDF
Are you preparing for an AI/ML Engineer / Data Scientist interview in 2026?
Every single AI/ML interview has a Python round. And most candidates fail it — not because they don't know Python, but because they don't know PYTHON FOR AI/ML.
There is a big difference.
Knowing Python: "I know loops, functions, and classes."
Python for AI/ML: "I use generators for batch data loading, NumPy broadcasting for vectorized preprocessing, custom sklearn transformers for leak-proof pipelines, and asyncio for concurrent LLM API calls."
This video covers 21 REAL Python interview questions asked at Google, Meta, Amazon, Microsoft, Anthropic, and top AI startups in 2025-2026 — with working code examples and interview tips.
Whether you are a fresher or experienced engineer — if you can't answer these, you will fail the Python round.
📥 FREE PDF DOWNLOAD: Get the full 21-question guide here → amanailab.com
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⏱️ TIMESTAMPS
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00:00 - Why Python for AI/ML is different from regular Python
02:01 - Section 1: Python Core for ML — Q1-Q3
24:40 - Section 2: NumPy Essentials — Q4-Q6
37:27 - Section 3: Pandas for ML — Q7-Q9
44:25 - Section 4: Data Preprocessing — Q10-Q12
54:24 - Section 5: OOP & Advanced Python — Q13-Q15
58:00 - Section 6: File I/O & Performance — Q16-Q18
1:04:18 - Section 7: Coding Challenges — Q19-Q21
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🎯 WHAT YOU'LL LEARN
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✅ List vs Tuple vs Set vs Dict — when to use each in ML (with O(1) vs O(n) difference)
✅ List comprehension vs generators — why generators are critical for large datasets
✅ *args and **kwargs — config-driven model creation pattern
✅ Why NumPy is 40-100x faster than Python lists
✅ Broadcasting — the core mechanism behind vectorized ML
✅ Copy vs View — the subtle bug that corrupts your training data
✅ apply() vs map() — and why vectorized is always better
✅ Missing values without data leakage (the #1 mistake freshers make)
✅ groupby() for feature engineering in fraud detection and churn prediction
✅ Outlier detection — IQR, Z-score, Isolation Forest
✅ Categorical encoding — One-Hot vs Ordinal vs Target encoding
✅ fit() vs transform() vs fit_transform() — the data leakage question
✅ Custom sklearn transformers with BaseEstimator + TransformerMixin
✅ Decorators — @timer, @retry with exponential backoff for LLM APIs
✅ Reading large CSVs efficiently — Parquet, chunking, dtype optimization
✅ Async API calls — embed 10K documents 10x faster with asyncio
✅ Multiprocessing vs multithreading — GIL explained simply
✅ Precision, Recall, F1 from scratch (no sklearn)
✅ K-fold cross-validation from scratch
✅ Complete sklearn Pipeline from scratch — the perfect interview answer
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🏢 COMPANIES ASKING THESE QUESTIONS
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Google · Meta · Amazon · Microsoft · Anthropic · OpenAI · Netflix · Apple · Stripe · Tiger Analytics · Airbnb · TCS · Infosys · Wipro · Top AI Startups
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📺 COMPLETE INTERVIEW PREP SERIES
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📚 FULL PLAYLISTS
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🎯 7 Days GenAI Interview Prep: • 7 Days Generative AI Interview Questions &...
🚀 60 Day GenAI Series: • 60 Days to Become a Generative AI Engineer
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🔗 CONNECT WITH ME
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✅ LinkedIn: / aman-chauhan71
✅ Instagram: / amanailab
🤝 Collaboration: [email protected]
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🚀 ABOUT AMANAI LAB
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AmanAI Lab is your go-to destination for mastering Generative AI in 2026. Daily tutorials, real-world projects, system design, and interview prep — built by an engineer for engineers.
🎯 Free AI Career Platform: amanailab.com
📚 Mentorship & Placement Program: DM me on LinkedIn
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