Choose CCAR-F most accurate study material

Your agent uses three tools: get_property_details(property_id) returns data including street address, get_price_history(property_id) returns historical pricing, and get_neighborhood_info(address) returns area statistics. You observe that get_neighborhood_info always requires get_property_details first just to extract the address, even when users specify the property by ID. This creates unnecessary latency and failure coupling - if the first call fails, the neighborhood request also fails. What tool design change best addresses this?

• A. Change get_neighborhood_info to accept property_id, resolving the address internally.
• B. Add retry logic and timeout handling to get_property_details.
• C. Consolidate into a single get_property_with_neighborhood(property_id) tool returning both datasets.
• D. Create a lookup_address(property_id) helper tool for retrieving addresses.

Production monitoring shows that the research phase takes longer than expected. Analysis reveals that the coordinator invokes the web-search subagent, waits for its response, and then invokes the document-analysis subagent. These tasks are independent; neither requires the other's output. How should you modify the system to run these subagents concurrently?

• A. Create an asynchronous orchestration layer that launches parallel threads, each running a separate coordinator-subagent pair, and then aggregates the results.
• B. Structure the coordinator to emit both Agent tool calls--for web search and document analysis--in a single response message instead of separate conversation turns.
• C. Add instructions explaining the performance benefits of parallel execution and request that the coordinator invoke both subagents simultaneously.
• D. Switch both subagents from a Sonnet-tier model to a Haiku-tier model to reduce their individual execution times.

Your get_portfolio_value tool returns the total value of a user's investment portfolio. You're deciding between returning a structured JSON object with explicit fields versus returning the information as a formatted text string. What is the primary advantage of using structured output with defined fields?

• A. Structured JSON consumes significantly fewer tokens than natural language, substantially reducing API costs.
• B. Structured JSON is processed deterministically by the model, significantly improving accuracy when extracting values.
• C. The agent can reliably extract specific values without parsing free-form text, reducing errors in subsequent operations.
• D. JSON schemas automatically validate that the underlying API returned correct data before the agent processes it.

You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your automated code review is missing genuine bugs in pull requests. Investigation reveals that your review prompt includes the instruction: "Only flag critical issues that would definitely cause production failures. Ignore minor concerns and anything you are uncertain about." Developers confirm that some missed bugs are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with each finding tagged with metadata, and actionable.
Which prompt change both removes the cause of the suppressed findings and preserves structured, tagged output for downstream filtering?

• A. Add a second review pass that rereads the diff using the same prompt, looking for anything the first pass may have missed.
• B. Enable extended thinking and instruct the model to reason step by step about every code change before producing its review.
• C. Remove all severity-related instructions from the prompt and let the model use its default judgment about what to report.
• D. Instruct the model to report all findings with confidence and severity tags, deferring filtering to a downstream step.

You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When researching "renewable energy adoption," the web search agent returns recent statistics (2024: 35% adoption) while the document analysis agent extracts data from internal reports (2021: 18% adoption). The synthesis agent incorrectly flags these as contradictory sources rather than recognizing the data shows growth over time. What change would best enable the synthesis agent to correctly interpret such temporal differences?

• A. Add a conflict resolution agent that automatically discards older data when newer data exists for the same metric.
• B. Configure the web search agent to only return results from the past 6 months.
• C. Instruct the synthesis agent to always treat the most recent data as authoritative and place older findings in a separate historical appendix.
• D. Require subagents to include publication or data collection dates in their structured outputs.