Every FDE interview question, grouped by topic.
Work through each track in order — they're sequenced the way real loops escalate. The first questions in every topic are free. New here? Start with the FDE interview questions overview. Sign in to track your mastery as you go.
LLM & GenAI Fundamentals
Tokens, context windows, prompting vs RAG vs fine-tuning, hallucinations, evals, cost and latency: the conceptual core every AI-native FDE loop probes first.
RAG & Agent System Design
Retrieval pipelines, chunking, reranking, tool-using agents, guardrails, multi-tenancy and eval harnesses: the modal FDE design round at OpenAI, Anthropic, Sierra, Glean and Scale.
Coding & DSA
Multi-part practical builds (in-memory DBs, rate limiters, parsers) plus the LeetCode-medium staples, calibrated to OpenAI/Anthropic practical screens and Meta/Palantir classics.
Machine Learning & Data Science
Classical ML, embeddings, metrics (precision/recall, AUC), experiment design and statistics: what Google Cloud, Databricks and Scale still test alongside GenAI.
SQL & Data Engineering
Window functions, gaps-and-islands, Spark internals, lakehouse design, idempotent pipelines and CDC: the Databricks/Snowflake/Palantir data track.
System Design & Production Engineering
Palantir-style decomposition, productionizing POCs, debugging customer incidents, observability, VPC/air-gapped deploys: where demos become deployments.
Behavioral & Customer Scenarios
Discovery role-plays, hostile-stakeholder scenarios, demo recovery, panel presentations and the 'why customer-facing' filter: the highest-variance, least-prepped FDE rounds.
MLOps & ML Engineering
CI/CD for models, drift detection and retraining, Kubernetes inference, feature stores, staging-to-production promotion and pipeline testing: what AWS, Databricks and every ML-platform loop drills.
ML Infrastructure & GPUs
GPU/TPU workloads, distributed training and parallelism, inference serving (vLLM, batching, KV cache), cluster scheduling and scaling API gateways: the infra depth NVIDIA, Google and the AI labs probe.
AI Security, Privacy & Governance
Prompt-injection defense in depth, PII handling, consumption tracking and abuse prevention, audit trails, SOC2/EU AI Act and enterprise data-governance questions every CISO-facing FDE must survive.
ML System Design (Product)
Design the model behind a product surface: feed ranking, recommendations, watch-time and ETA prediction, fraud and bot detection, ads ranking. Candidate generation, feature and label design, training, offline and online evaluation, and serving at scale, the way Meta, Google, Netflix and recsys teams run the round.