Claude Certified Architect – Foundations Practice Test

How to Study for the Claude Certified Architect – Foundations Exam

Anchor your study to the five blueprint domains, weighted as the exam weights them. Agentic Architecture & Orchestration (27%) is the biggest: know the agentic loop cold — continue while stop_reason is "tool_use", append tool results to history, terminate on "end_turn" — plus the hub-and-spoke coordinator pattern, subagent context isolation (subagents never inherit the parent's history; pass findings explicitly in the prompt), parallel spawning via multiple Task calls in one response, and when hooks (deterministic enforcement) beat prompt instructions (probabilistic guidance). The exam loves the hooks-vs-prompt distinction: if a business rule must never be violated, the answer is programmatic enforcement.

For Tool Design & MCP (18%), remember that tool descriptions are the model's primary selection signal — most misrouting questions are answered by 'fix the descriptions' or 'reduce/scope the toolset,' not by routing layers or temperature. Know structured MCP error responses (errorCategory, isRetryable, isError in the result so the model can self-correct), tool_choice semantics (auto/any/forced), and .mcp.json project scoping with ${ENV_VAR} expansion for secrets. For Claude Code (20%), map the memory hierarchy (user ~/.claude/CLAUDE.md is per-machine; project CLAUDE.md ships with the repo — the answer to every 'works on my machine, fails in CI' question), .claude/rules/ with paths globs, skill frontmatter (context: fork isolates verbose output; allowed-tools scopes permissions), plan mode vs direct execution, and headless -p with --output-format json for CI.

For Prompt Engineering & Structured Output (20%), the recurring patterns are: explicit categorical criteria beat vague instructions (and beat 'be conservative') for false-positive problems; few-shot examples fix format inconsistency across varied documents; tool use with a JSON schema guarantees syntax but never semantics; nullable fields prevent fabrication when data is absent; retry-with-error-feedback cannot recover information missing from the source; and the Message Batches API trades a 24-hour window for a 50% discount, with custom_id as the only reliable way to match results. For Context Management & Reliability (15%), know the persistent case-facts block (vs lossy progressive summarization), lost-in-the-middle positioning (documents first, question last), trimming verbose tool outputs, objective escalation triggers (explicit human request, policy gaps — never sentiment or self-reported confidence), and distinguishing access failures from genuinely empty results in multi-agent error propagation. Finally, drill with our timed 60-question mock to build pacing for the real 120-minute format.