AI Engineer & FDE Interview Prep Practice Tests

About this course

You are preparing for AI Engineer and Forward Deployed Engineer (FDE) interviews. AI Engineer roles focus on building applications on top of foundation models: retrieval, agents, evals, and production reliability. FDE roles add customer-facing problem decomposition, scoping under ambiguity, and shipping under real constraints at companies that hire forward-deployed talent.

This course is practice tests only. It does not replace hands-on projects or live mock interviews. It gives you timed, scenario-style questions so you can measure readiness, find weak areas, and practice the judgment interviewers score.

What these interviews typically coverAI Engineer technical pillarsLLM fundamentals: tokens, context windows, sampling, prefill and decode behavior, and enough model mechanics to reason about cost and latency without training models from scratch.

RAG and retrieval: chunking, embeddings, vector search, hybrid retrieval, reranking, recall and precision, and debugging retrieval before blaming the model.

Agents and tool use: when an agent is justified versus a fixed workflow, tool design, error compounding, memory and state, and patterns such as MCP where relevant to the role.

Evals and reliability: golden sets, LLM-as-judge calibration, trajectory versus outcome metrics, pass rates, and gating changes before production.

LLMOps and production: cost and latency trade-offs, caching, guardrails, monitoring, failure modes, and rollout strategy for AI features.

FDE-focused skills

Problem decomposition: scoping a vague customer or enterprise brief before proposing a solution. Interviewers often fail candidates who jump to architecture without clarifying users, data, constraints, and success criteria.

Customer and deployment scenarios: phased plans, discovery, compliance and data residency, handoff, and what you would measure in the first week versus the first quarter.

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