Real questions from Google, Meta, Amazon, OpenAI, and Anthropic. Covering ML, GenAI, Agentic AI, and System Design — with detailed answers.
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Core Python, NumPy, Pandas, and data manipulation questions commonly asked at top tech companies. Covers everything from language internals to high-performance data wrangling.
Descriptive statistics, probability theory, hypothesis testing, and Bayesian methods — the mathematical foundation every data scientist must master.
Core ML algorithms, model evaluation, feature engineering, and the practical trade-offs interviewers expect you to articulate.
Neural network architectures, training dynamics, optimization, regularization, and the practical know-how that separates textbook knowledge from production experience.
Natural language processing from classical text processing through transformers and modern fine-tuning techniques. Covers tokenization, embeddings, attention mechanisms, BERT/GPT architectures, and parameter-efficient fine-tuning.
Modern generative AI from large language models through prompt engineering, RAG systems, hallucination mitigation, and evaluation methods.
Master the architecture, tooling, and production patterns behind autonomous AI agents — from single-agent ReAct loops to multi-agent orchestration and the Model Context Protocol.
From model serving and monitoring to ML pipelines, distributed training, and responsible AI — everything you need to deploy and maintain ML systems at scale.
Master SQL querying from fundamentals through advanced window functions, data modeling, and ETL pipeline design. Covers real questions from Meta, Google, Amazon, Uber, Airbnb, and Stripe.
Design production ML systems end-to-end: recommendation engines, search ranking, fraud detection, real-time inference, and content moderation. Covers architecture patterns from Netflix, Google, Uber, and Meta.
Cost optimization and latency management for production LLM systems.
Model selection, routing, and output quality for production GenAI.
Production-grade retrieval-augmented generation — chunking, embedding strategies, hybrid search, ANN algorithms, vector database internals, and systematic evaluation. Built for engineers who have shipped RAG beyond the demo.
Fine-tuning methods, RLHF and alignment techniques for LLMs.
Advanced prompt engineering and LLM evaluation techniques.
Core and advanced prompt engineering techniques for LLM systems.
Orchestration patterns and communication in multi-agent AI systems.
Real incident response and stakeholder scenarios from production AI systems.
Production architecture decisions and debugging agent system failures.
Reasoning models, MCP, computer use, and frontier AI topics from 2025.
Multimodal AI, open source models, and local deployment patterns.
Bias detection, fairness metrics, safety guardrails and content moderation.
Privacy, data security, regulatory compliance and AI governance frameworks. The questions every AI engineer gets asked once their work starts touching real users, regulated industries, or the wrong side of a legal brief.
ROI modeling, build-vs-buy frameworks, AI team design, and stakeholder communication for product and engineering leaders making real AI investment decisions.
System design for LLM infrastructure and real-world AI products. How to architect serving stacks that handle thousands of concurrent users, and how to design the full product systems that interviewers at AI-first companies actually ask about.
Data pipelines for AI systems and scaling patterns for production.