Claude Certified Architect Foundations Practice Exam Questions and Answers – Part 18/20

Answer: D. Rewrite each tool definition with detailed descriptions covering use cases, non-use cases, inputs, returned data, and caveats.

Rewriting tool definitions with detailed use cases and boundaries is the highest-leverage fix for incorrect tool selection among similar capabilities. Shorter names or isolated examples do not provide the clear decision boundaries the model needs.

Answer: C. Require each subagent to return structured claim records with source location, publication date, evidence excerpt, and methodology notes.

Requiring structured claim records forces subagents to pass provenance and methodology to the synthesizer explicitly. Relying on concise summaries permanently destroys the underlying evidence needed to weigh conflicting claims.

Answer: A. Add Task to the coordinator SDK run's allowedTools or allowed_tools list.

Adding Task to the allowed tools list authorizes the coordinator to execute subagent delegation without pausing. Adding Agent only to worker subagents fails because the interactive pause happens before workers are ever spawned.

Answer: B. Rename the tools with source-specific names, rewrite each description with use and non-use boundaries, and update the dispatcher for the new names.

Renaming the tools with source-specific names and defining clear use boundaries directly resolves overlapping description ambiguity. Merging them into a generic tool violates the requirement to maintain separate output contracts and audit logs.

Answer: C. Declare the server's resources capability, expose catalog metadata through resources/list, and read selected URI contents with resources/read.

Using MCP resources lets the host application control context attachment via list and read primitives. Forcing Claude to call discovery tools wastes conversation turns and directly violates the team's context loading requirement.

Answer: C. Use dynamic adaptive decomposition so Claude inspects the module, creates subtasks from findings, and synthesizes the test plan.

Dynamic adaptive decomposition is optimal for unfamiliar legacy code because subtasks depend on discovered dependencies. Fixed prompt chaining fails because it assumes a rigid sequence of steps that may not apply to the specific module.

Answer: B. Add .claude/rules/database-migrations.md with YAML frontmatter paths: ["**/migrations/**/*.{sql,ts}"] and place the migration review checklist there.

Using YAML frontmatter in a rules file correctly scopes the migration checklist to relevant file paths, preventing false positives. Placing rules in the root CLAUDE.md applies them globally, which continues to generate irrelevant review noise.

Answer: D. Have the coordinator split the request into dimension-specific subtasks, pass shared constraints to each, invoke independent specialists together, and synthesize their findings.

Splitting the request into dimension-specific subtasks allows independent specialists to execute in parallel, drastically reducing latency and improving coverage. Keeping one broad subtask or disabling parallel tool use creates unnecessary sequential bottlenecks for independent workstreams.

Answer: B. Prepend a concise findings map before the aggregated outputs, then group detailed evidence under explicit source and topic headers.

Prepending a concise findings map mitigates lost-in-the-middle attention limits by placing critical cues at the top, guiding the model through the entire bundle. Explicit source and topic headers further help the model navigate dense context without dropping provenance.

Answer: B. Add downstream business-rule validation before refund execution and feed specific failures back for correction.

Tool schemas guarantee the correct JSON shape, but they cannot enforce business logic. You must add downstream code to validate domain rules like field semantics and totals, returning failures to the model for correction.

Answer: B. Return a CallToolResult with isError: true and place custom errorCategory, isRetryable, and policy text in structuredContent.

Using the official MCP error indicator clearly signals the agent that execution failed, preventing blind retries. Placing custom metadata in structured content allows the agent to evaluate retryability and understand the specific policy failure.

Answer: B. Start with a discovery pass that maps the topic structure and high-impact areas, then assign prioritized subtasks that can change as findings reveal dependencies.

Open-ended research benefits from separating exploration from execution. A discovery pass maps the problem space, enabling the coordinator to create dynamic subtasks and adapt when early findings reveal gaps or dependencies.

Answer: B. Persist and resend the relevant messages array, including earlier assistant outputs, tool results, and the current user turn.

The Messages API is stateless, so Claude cannot implicitly remember prior turns. Persisting and resending the relevant messages array provides the historical context needed for the model to synthesize findings consistently.

Answer: C. Emit per-field confidence values in structured output, calibrate thresholds on labeled examples, and route low-confidence fields to reviewers.

Emitting per-field confidence values targets reviewer attention precisely where risk is highest. Calibrating thresholds using a labeled validation set ensures raw model scores accurately reflect observed accuracy for each specific field.

Answer: A. Add a PostToolUse hook that reads tool_response and returns hookSpecificOutput.updatedToolOutput with normalized fields.

A PostToolUse hook runs after a successful tool call, making it the right place to transform data. Using the updatedToolOutput field replaces the raw response with normalized fields, ensuring Claude reasons over canonical formats.