Claude Certified Architect Foundations Practice Exam Questions and Answers – Part 2/4

Answer: A. Replace fetch_url with a load_document tool that accepts catalog document IDs or approved URLs and validates before fetching.

Replacing the generic tool with a constrained one directly prevents the subagent from fetching arbitrary URLs. Relying on prompt instructions to avoid links is a weaker safeguard because the model may still attempt to retrieve unapproved resources.

Answer: D. Require each subagent to return structured handoff records separating findings, file paths, symbols, line ranges, and evidence snippets.

Structured handoff records preserve attribution by separating findings from the metadata needed to verify them. Asking the synthesizer to reconstruct missing file paths from prose is unreliable because lost context cannot be recovered deterministically.

Answer: C. Use Grep to search file contents for the error string and function names, then Read the matching files.

Using Grep to search file contents is the correct first step because it efficiently targets the specific error string and function names. Reading files upfront or using Glob wastes context and ignores the actual file contents.

Answer: B. Use a fixed prompt chain for stable review rubrics, and let the agent adapt subtasks during open-ended investigations.

Stable workflows benefit from fixed prompt chains, while open-ended investigations require adaptive task planning. Using a single rigid checklist for both is an anti-pattern because bug discovery dictates subsequent steps.

Answer: D. Have Claude first ask targeted questions about invariants, invalidation triggers, audit requirements, and failure modes before proposing implementation.

Using the interview pattern surfaces hidden constraints and invalidation rules before implementation begins. Immediately writing code is a high-risk anti-pattern because Claude might fill compliance gaps with incorrect defaults.

Answer: A. Send a follow-up request containing the document, failed extraction, and exact validation errors for targeted self-correction.

Providing the exact validation errors alongside the failed extraction gives Claude the context needed for targeted self-correction. Blindly retrying the original prompt or lowering temperature fails because the model lacks the specific feedback required to fix semantic mistakes.

Answer: D. Run Claude Code with –output-format json and –json-schema, then map validated findings to inline PR comments.

Using explicit JSON output formats with a schema forces Claude to produce reliable, machine-readable data. Relying on regex parsing over markdown is a brittle anti-pattern because natural language formatting varies unpredictably across runs.

Answer: B. Add explicit human-review criteria with few-shot examples distinguishing absent optional fields, unreadable required fields, and unsupported document types.

Adding explicit human-review criteria with few-shot examples clarifies the decision boundaries between absent optional fields and unreadable required values. Global confidence thresholds fail because they cannot distinguish between structurally missing data and extraction errors.

Answer: C. Run focused document-level evidence checks first, then perform a separate cross-document consistency pass before synthesis and reporting.

Splitting the workflow into focused document-level checks followed by a cross-document consistency pass prevents attention dilution. Processing all sources in a single prompt causes lost-in-the-middle effects, leading to uneven validation quality.

Answer: B. Keep synchronous calls for onboarding forms, and use the Message Batches API for historical contracts with latency-tolerant processing.

Matching the API to the latency requirement means keeping synchronous calls for real-time onboarding screens. The Message Batches API is perfect for historical contracts because it reduces costs for asynchronous, latency-tolerant workloads.

Answer: B. Split the guidance into focused topic files under .claude/rules/, such as testing.md, api-conventions.md, and deployment.md.

Organizing topic-specific guidance in .claude/rules/ preserves shared repository-level configuration while avoiding a single oversized file. This directly addresses maintainability and review concerns without requiring developers to manually invoke commands or copy personal settings.

Answer: B. Revise coordinator planning to identify plausible entry points, then delegate distinct code areas to subagents before synthesis.

The symptoms show that subagents completed their assigned work, but the assignments covered only the HTTP path. Improving the coordinator's initial planning helps ensure broad codebase questions are decomposed across relevant entry points before synthesis begins.

Answer: C. Define an extraction tool with a JSON schema for target fields, then consume the structured tool_use input directly.

Tool use with a JSON schema is the most reliable way to obtain schema-compliant structured output from Claude. Instead of parsing generated text, the application reads the structured input from the tool_use block, eliminating common JSON syntax failures like markdown fences or trailing commentary.

Answer: C. Return failed tool calls with isError set true, plus concise diagnostic content the subagent can use for recovery.

MCP tools should distinguish failed executions from successful content using the isError pattern. This lets the agent treat the response as a tool failure rather than as research material, enabling retries, alternative searches, or coverage-gap annotations.

Answer: B. Use Glob with patterns like <code>**/*.fixture.json</code> to enumerate matching paths, then Read only the files that require inspection.

Glob is the appropriate built-in tool when the selection criterion is based on file names, extensions, or path patterns. It returns matching paths directly, reducing noise and allowing the agent to read only the relevant files.