Practice the judgment, not the answer.
Every question in this bank tests a real production tradeoff in agentic AI — the kind of decision you'll face when you build, evaluate, or commission these systems in your career. The wrong answers aren't filler; each one is tagged with the specific misconception a smart student might hold.
Before you see the answer, you'll be asked to type your reasoning. That's the part that builds the judgment. Take your time.
Agentic Architecture & Orchestration
How agents loop, when to plan, how subagents coordinate, where deterministic enforcement beats prompting.
Tool Design & MCP Integration
Why descriptions are leverage, when narrow tools beat general ones, how to scope MCP across teams.
Claude Code Configuration & Workflows
Where conventions live, when to plan, how teams share rules, how to run Claude Code non-interactively.
Prompt Engineering & Structured Output
Schema constraints over instructions, categorical criteria over adjectives, examples for format variation.
Context Management & Reliability
Lost-in-the-middle, structured errors, honoring explicit requests, stratifying before automating.
Knowledge Agents & RAG
When to ground agents in your data, retrieval failure modes, "I don't know" patterns, and how to evaluate retrieval quality.
Safety, Guardrails & Domain Risks
Approval gates, prompt injection defense, output filtering, and domain-specific risks across HR, finance, and marketing.
Evaluation, Testing & LLM-as-Judge
Building test suites, calibrating LLM-as-Judge, measuring consistency, and answering "does it work?" with data.