Claude Certified Architect Foundations Practice Exam Questions and Answers – Part 16/16

Answer: C. Start a fresh session and inject a structured summary of validated findings, decisions, and sources to recheck.

Starting a fresh session avoids carrying stale tool results and outdated documents back into the model context. Injecting a structured summary preserves validated findings without risking hallucination from instructing the model to ignore old context.

Answer: A. Append the assistant message with the tool requests, then append one user message containing both matching tool results before the next request.

Appending the assistant message and then a single user message with both tool results maintains the exact conversation state required. The API expects all tool results for a given turn to be returned together in one user message so the model can map them correctly.

Answer: D. Rewrite each tool description to define use cases, non-use cases, input formats, example queries, returned data, and edge cases.

Tool descriptions are the highest-leverage signal Claude uses when deciding which tool to call. Broad instructions or tool renames do not directly repair the model's decision boundary between overlapping tools like explicit descriptions do.

Answer: D. Add Agent to the coordinator's allowedTools or allowed_tools configuration.

The documented tool for spawning a subagent is Agent, and SDK configurations must include it in allowed tools for auto-approval. Simply allowing the subagent names directly will fail to trigger the native delegation tool without explicit invocation setups.

Answer: A. Define the tools with clear descriptions and schemas, then run a loop that executes each tool_use request and returns matching tool_result blocks.

This uses the documented tool use contract where Claude decides which client tools to call, while the application executes them. Fixed controllers or forced tool choices prevent the model from dynamically adapting its path based on the document.

Answer: B. Require each subagent to return structured evidence records with the claim, source location, document date, and method context.

Requiring structured evidence records directly improves the handoff by preserving provenance and methodology before summarization compresses the details. Downstream synthesis only receives what the coordinator passes forward, so prose summaries often lose critical context.

Answer: B. Submit the review prompts as batch items, retrieve any tool_use results by custom_id, run the requested client tools, and send follow-up requests with tool_result blocks.

The Message Batches API processes asynchronously, so your application must execute any client tools after retrieving the initial results. You then send follow-up requests containing the tool results to complete the workflow. Batch workers cannot execute local code directly.

Answer: B. Create ~/.claude/skills/my-code-review/SKILL.md and invoke it as /my-code-review.

Placing a uniquely named skill directory under the personal tilde slash claude path ensures it remains private to you and avoids overriding the shared project skill. Project-level files are shared, whereas user-level files apply only to your local machine.

Answer: A. Run claude -p with –output-format json and –json-schema, then parse structured_output for the PR comment payload.

Using print mode with a JSON schema strictly enforces the required fields for the downstream script by returning guaranteed valid data in structured output. Plain text outputs lack programmatic reliability and break easily in automated pipelines.

Answer: A. Delegate focused read-only investigations to subagents, then use their returned summaries in the main conversation.

Delegating focused read-only investigations to subagents isolates the noisy exploration logs from the main coordination thread. Pasting full transcripts directly into the main conversation defeats this goal by consuming context window space with irrelevant details.

Answer: C. Run Claude Code with -p, –output-format json, and –json-schema, then read the validated result from structured_output.

Print mode with JSON output and a schema ensures the PR bot receives strictly validated data inside structured output while retaining top-level metadata. Without schema enforcement, relying on prompt instructions alone risks posting malformed comments.

Answer: C. Maintain a structured scratchpad of exact facts that is updated separately from compacted conversation history.

Maintaining a separate structured scratchpad of exact facts protects critical numerical values from being degraded during conversation compaction. Relying on generalized summaries inevitably leads to broken logic when precise constants are required.

Answer: D. Replace the vague flagging instruction with measurable rules for when parsed line items conflict with stated totals.

Replacing subjective instructions with specific, measurable rules eliminates ambiguity and ensures consistent flagging of arithmetic mismatches. Broad instructions cause the model to guess inconsistently, leading to false positives and missed errors.

Answer: A. Check stop_reason, retry truncated generations with a larger output limit, then return specific semantic validation errors for correction.

Checking the stop reason handles truncation by prompting a retry with an expanded token limit. Relying solely on syntax validation fails here because semantic validation is required to catch nonexistent line numbers and correct the model.

Answer: A. Read the current file, construct the complete desired file contents, then overwrite it with Write.

Reading the file and using the Write tool to overwrite it with the exact desired contents provides a reliable last-resort edit when exact matching fails. Using replace all incorrectly modifies every matching snippet instead of making a single targeted change.