Claude Certified Architect Foundations Practice Exam Questions and Answers – Part 7/8

Answer: B. Create a focused rule file with YAML frontmatter paths for both migration globs, containing only the migration review checklist.

Using a focused rule file with YAML frontmatter path globs ensures instructions load only for matching files. Path-scoped rules prevent irrelevant warnings in TypeScript reviews while keeping automated guidance active for SQL migrations.

Answer: C. Add a tool-call gate that rejects process_refund until get_customer and lookup_order return matching, refund-eligible records.

A programmatic tool-call gate deterministically enforces the required validation sequence before refunds execute. Relying solely on system prompt instructions is risky for business-critical safety, whereas a gate guarantees eligibility checks without blocking valid cases.

Answer: C. Store the command markdown under .claude/commands/ in the repository, then commit it with the CI workflow changes.

Storing custom commands inside the project's dot-Claude directory ensures they are version-controlled and available from a fresh checkout. Keeping them in a user's home directory prevents standard CI runners and new contributors from accessing the workflow.

Answer: D. Before launching each new investigation wave, create a structured findings brief and include it in every subagent's initial prompt.

Providing a structured findings brief in each subagent prompt explicitly passes accumulated context across isolated boundaries. Relying on subagents to rediscover earlier constraints wastes effort and risks inconsistent conclusions, so explicit context passing is essential.

Answer: C. Revise each tool description to state accepted identifiers, example customer phrases, edge cases, and boundaries versus similar tools.

Clear tool descriptions provide the necessary context for the model to select the right tools during ambiguous conversations. Hard-coded decision trees or keyword routers are brittle, whereas detailed descriptions help the model distinguish overlapping features naturally.

Answer: C. Use fork_session from the verified case baseline, explore each resolution path independently, then compare branch summaries before deciding.

Forking a verified session preserves the shared factual baseline while exploring divergent reasoning paths in isolated contexts. Exploring multiple paths in a single conversation causes assumptions to bleed between branches, leading to flawed comparisons.

Answer: C. Return distinct structured categories for transient, validation, business, and permission failures, with retryability flags and customer-safe recovery guidance.

Returning distinct structured categories for each failure type gives the model actionable context to choose the right recovery path. Collapsing all errors into a single result prevents the agent from distinguishing transient timeouts from hard policy denials.

Answer: B. Keep tool-based structured output, then add semantic validation against diff metadata, severity rules, and finding consistency before publishing comments.

Semantic validation against pull request diff metadata ensures that model outputs meet actual review policies before publishing. Structured JSON schemas only enforce formatting compliance, so logic checks are still needed to catch invalid line numbers or logical contradictions.

Answer: B. Have the coordinator compare the draft resolution with the original request, assign targeted follow-up subagent work, then rerun synthesis.

An explicit coordinator review step compares draft resolutions against original requests to identify incomplete answers. This allows targeted follow-up queries to subagents, ensuring completeness without unnecessarily escalating complex cases to humans.

Answer: A. Separate findings by category and severity, tighten criteria for noisy checks, and prioritize high-precision issues in reviewer queues.

Separating findings by category and severity directly fixes reviewer fatigue by preventing noisy alerts from burying high-precision flags. Tightening criteria for ambiguous categories ensures reviewers maintain trust and focus on critical confirmed mismatches.

Answer: B. Require confidence per critical record field, then calibrate routing thresholds using a labeled set of previously reviewed changes.

Field-level confidence scores expose hidden uncertainty in high-risk areas like rollback and security. Calibrating routing thresholds against labeled review data prevents a high overall score from masking critical blind spots, whereas a single aggregate score lacks the needed precision.

Answer: B. Create a .claude/rules/ migration file with paths matching all migration patterns so guidance loads only for those files.

Path-scoped rule files are ideal for enforcing conventions by file pattern rather than directory hierarchy. This setup keeps migration guidance automatic for matching files across the monorepo while preventing irrelevant context from loading during unrelated edits.

Answer: C. Use a prompt chain that reviews each order or charge independently, then runs a final account-level consistency pass.

A prompt chain matches this predictable workflow by isolating each local item before running a final account-level synthesis. Separating per-item review from broader reconciliation reduces attention dilution and catches interactions across issues before responding.

Answer: C. Use small package-level CLAUDE.md files that reference the relevant standards documents with @import, keeping shared root guidance minimal.

Using small package-level files with @import keeps package guidance close to the code while avoiding copied instruction drift. This allows maintainers to load only relevant content, reducing context bloat and ensuring standards remain canonical and updatable.

Answer: B. Configure tool_choice to force extract_pr_metadata on the first request, then run review findings in a follow-up turn.

Forcing a specific tool choice is the safest mechanism when a structured extraction must happen before later steps. It guarantees the first response calls the metadata tool, allowing the pipeline to use those results in a follow-up request for generating findings.