Claude Certified Architect Foundations Practice Exam Questions and Answers – Part 1/4

Answer: C. Document team testing standards, high-value test criteria, and reusable fixtures in project CLAUDE.md consumed by the CI job.

Project-level context files like CLAUDE.md are the correct place to define team-wide testing standards and available fixtures, ensuring consistent CI guidance. Relying on individual developer prompts or increasing test volume does not solve the underlying context gap and will still produce superficial or duplicate tests.

Answer: B. Run the review in a separate Claude Code invocation with the PR diff, repository context, and review criteria only.

Running the review in a separate Claude Code invocation provides a clean perspective focused solely on the diff and repository criteria, avoiding confirmation bias. Reusing the generation session makes the model defend its prior reasoning, which blinds it to subtle architectural flaws.

Answer: D. Share the failing test names, inputs, expected versus actual outputs, and stack traces, then iterate on minimal fixes.

Sharing specific failing test names, inputs, and expected versus actual outputs turns vague feedback into a concrete, test-driven loop. Subjective prompts like asking the model to be more careful do not provide actionable targets, leading to inconsistent fixes and unrelated code rewrites.

Answer: C. Create tests for expected behavior, edge cases, and performance constraints first, then iterate by sending Claude failing results.

Defining tests for expected behavior and edge cases before implementation creates objective acceptance criteria that catch subtle bugs early. Feeding specific failing test outputs back to the model ensures precise, incremental corrections instead of the uneven results caused by long prose specifications.

Answer: A. Send one detailed refinement request describing all observed failures, shared constraints, and expected behavior across the affected call paths.

When interacting failures share constraints across the same template, submitting one detailed refinement request gives the model the full context needed to avoid regressions. Fixing issues sequentially in separate sessions blinds the model to dependencies, often reintroducing previously resolved bugs.

Answer: C. Have the coordinator maintain shared investigation state and include relevant prior findings in each subsequent subagent prompt.

The correct answer makes the coordinator responsible for passing shared context into each subagent prompt. For the exam, remember that subagents do not automatically inherit prior conversational state, so the orchestrator must explicitly provide it.

Answer: C. Call escalate_to_human with the order context and brief summary, without attempting autonomous lookup or replacement processing first.

The correct choice honors an explicit human request by transferring immediately with context. Avoid the tempting autonomous lookup option, because forcing automation when a user explicitly requests a person breaks trust and violates core escalation policy design principles.

Answer: C. Rewrite the QA prompt to define reportable errors, acceptable variations, and skip conditions with concrete examples for each category.

Defining explicit reportable errors with concrete examples directly fixes high false positive rates. Vague instructions to be conservative fail because the model lacks objective boundaries for acceptable variations versus actual extraction mistakes.

Answer: B. Add a coordinator review loop that checks synthesis coverage, redelegates targeted follow-ups, and reruns synthesis until quality criteria are met.

Adding a coordinator review loop actively identifies gaps and triggers targeted follow-ups before report generation. Fixed iteration caps fail because they stop blindly regardless of remaining coverage gaps, while hiding low-coverage sections obscures rather than resolves the issue.

Answer: B. Move the required catalog server into project-scoped .mcp.json, and keep the experimental notes server in user-scoped ~/.claude.json.

Shared team servers belong in project-scoped configuration so they sync automatically via source control. User-scoped configuration isolates experimental tools, whereas manual setup commands or user-scoped files invite drift and configuration inconsistency across the team.

Answer: C. Return isError true with errorCategory business, isRetryable false, and guidance to request summaries or short excerpts instead.

Returning a non-retryable business error with actionable guidance tells the coordinator to change its approach. Treating this as transient or a credential failure wastes tokens on retries since the license restriction is a permanent business rule, not a technical fault.

Answer: C. Move the shared conventions into a project-level CLAUDE.md file and commit it so every clone loads them.

Committing a project-level configuration file guarantees that all cloned repositories receive the shared conventions automatically. Relying on manual copying or user memory files breaks consistency, while slash commands require manual invocation that engineers might forget.

Answer: A. Have Claude ask targeted clarification questions about operational constraints, ownership assumptions, and edge cases before proposing implementation changes.

Having the agent ask targeted clarification questions resolves hidden requirements before any code is written. Relying on code review to catch missing constraints is inefficient, and adding global instructions creates rigid rules that conflict with team-specific variations.

Answer: A. Randomly sample high-confidence claims across topic, source type, and report section, then track errors and new patterns by stratum.

Stratified random sampling exposes hidden error rates in specific topics or source types that aggregate metrics hide. Reviewing only low-confidence claims is risky because overconfidence in niche segments can let systematic defects reach production undetected by overall dashboards.

Answer: C. Preserve both statistics with source attribution, methodology notes, and a conflict annotation for the report generator to present transparently.

Preserving both statistics with methodology notes allows transparent reporting instead of forcing a false consensus. Averaging conflicting metrics is mathematically invalid when underlying definitions differ, and suppressing the smaller study hides critical context from readers.