Claude Certified Architect Foundations Practice Exam Questions and Answers – Part 3/4

Answer: D. Add targeted few-shot examples showing the fields extracted from cover-letter text, footnotes, and table captions with brief rationale.

Targeted few-shot examples are the best fit when extraction failures cluster around varied document structures. Showing examples where fields appear in cover letters or footnotes teaches Claude the intended judgment pattern, whereas forcing non-null values would encourage hallucinated data.

Answer: C. Run focused review passes on each packet section, then run a separate consistency pass across cross-section fields.

Running focused review passes on each packet section reduces attention dilution and improves coverage of local field errors. A separate consistency pass then focuses on relationships across sections, such as matching tax IDs and signatures, without competing with local checks.

Answer: B. Start a fresh session with a structured summary of still-valid findings, then re-run targeted searches and analyses for changed sources.

Starting fresh avoids carrying stale tool outputs and outdated subagent observations forward as if they were still authoritative. Injecting a structured summary preserves useful context while forcing updated evidence to be gathered where source validity has changed.

Answer: C. Expose the support policy index and knowledge-base catalog as MCP resources, while keeping tools for selected record actions.

MCP resources are the correct tool for exposing read-only reference catalogs, giving the agent immediate context without wasting turns on exploratory tool calls. A practical exam cue is to match static data to resources and dynamic state changes to tools, avoiding bloated system prompts or mandatory startup calls.

Answer: C. Use plan mode to explore dependencies, compare viable migration strategies, and produce an implementation plan before modifying files.

Using plan mode allows Claude to explore dependencies and design an implementation strategy before modifying files, which is critical for broad architectural changes. Jumping straight to execution risks breaking unrelated workflows because the model lacks a structured blueprint for complex multi-file migrations.

Answer: A. Rewrite each MCP tool description to specify purpose, accepted identifiers, returned fields, edge cases, and boundaries versus related tools.

Rewriting MCP tool descriptions to clarify their purpose, accepted inputs, and boundaries is the most direct fix for unreliable model-driven tool selection. Relying on hardcoded keyword routers or merging tools obscures functionality and defeats the purpose of granular, descriptive tool architecture.

Answer: D. Force the metadata extraction tool with tool_choice for the first request, then continue subsequent analysis in follow-up turns.

Forcing a specific tool call using tool choice is the most reliable way to guarantee a required initial step executes before subsequent model-driven analysis. Prompting alone is probabilistic, and setting tool choice to any does not ensure the correct prerequisite tool is selected.

Answer: A. Treat multiple customer matches as unresolved identity and request a disambiguating identifier before any order or refund operations.

Treating multiple customer matches as an unresolved identity forces the agent to request a disambiguating identifier before taking sensitive actions, safely preventing account mixups. Routing all duplicate records to humans breaks automation goals, while probabilistic heuristics remain too risky for refunds.

Answer: B. Add a structured detected_pattern field to each QA finding, then analyze acceptance rates by pattern and document type.

Adding a structured detected pattern field captures the specific trigger for each finding, making the feedback loop analyzable. Segmenting outcomes by pattern and document type reveals exactly which prompts or validators need refinement, whereas globally raising confidence thresholds suppresses useful categories indiscriminately.

Answer: D. Require summaries and handoffs to carry structured claim-source mappings with source type, excerpt or tool field, timestamp, and verification status.

Structured claim-source mappings preserve provenance as information moves through summarization and escalation, ensuring facts are distinguished from inferences. Relying on shorter narratives or confidence filtering increases provenance loss and hides uncertainty, making it an anti-pattern for multi-source workflows.

Answer: C. Require subagents to return structured handoff records with findings separated from file paths, symbols, line ranges, commands, and source excerpts.

Structured handoff records preserve both discovered content and the metadata needed to verify it later. Separating facts from attribution metadata helps subagents use findings accurately without losing file locations, whereas stripping source details or relying on narrative summaries degrades downstream reliability.

Answer: A. Split the request into distinct concern threads, investigate them in parallel with shared constraints, then synthesize one unified cross-sector report.

Splitting the request into distinct concern threads ensures balanced coverage while keeping shared constraints consistent across the investigation. A final unified synthesis step resolves contradictions before report generation, avoiding the attention dilution and conflicting recommendations caused by single-pass sequential processing.

Answer: D. Update each subagent's output contract to attach dates, source locations, and methodological notes to every extracted finding.

Updating output contracts to attach dates, source locations, and methodological notes preserves critical context at the finding level. This prevents important qualifiers from being lost during handoffs, whereas passing full transcripts risks attention problems and cleanup passes might hallucinate missing provenance.

Answer: C. Add a prerequisite gate that rejects process_refund until verified customer, eligible order, and computed refund amount are present in state.

Adding a prerequisite gate creates a deterministic control point for workflow ordering, preventing financially risky actions until state requirements are met. Relying on system prompts or few-shot examples remains probabilistic, whereas a hardcoded gate guarantees compliance regardless of model reasoning.

Answer: D. Analyze accuracy by document type and extracted field, then automate only segments meeting validated thresholds while routing others to review.

Analyzing accuracy by document type and field exposes weak performance clusters that strong aggregate scores hide. Segment-level validation lets you safely automate reliable areas while preserving human review for underperforming slices, avoiding the false confidence of relying strictly on global metrics.