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Anthropic Claude Certified Architect - Foundations 認定 CCAR-F 試験問題：

問題 #1
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer asks the agent to find all callers of a function before removing it. The function is defined in a core library but is also exposed through wrapper modules that rename the function for domain- specific use (e.g., calculateTax in the library becomes computeOrderTax in the orders module).
What exploration strategy will most reliably identify all callers?

A. Use Grep to find all files that import from the library or wrapper modules, then read each file to check whether it uses the function.
B. Use Grep to search for the function's original name across the codebase.
C. Search for the function name in project documentation to understand intended usage patterns and navigate to documented integration points.
D. Read the library and wrapper modules to identify all exposed names for the function, then Grep for each name across the codebase.

問題 #2
Which statement BEST describes Retrieval-Augmented Generation?

A. It compresses model weights.
B. It supplies relevant external knowledge during inference.
C. It permanently retrains Claude.
D. It changes token pricing.

問題 #3
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline occasionally receives responses that cannot be parsed as valid JSON, causing downstream processing failures. The current implementation prompts Claude to return JSON in the response text and then parses it.
What is the most reliable approach to ensure Claude returns valid, schema-compliant structured data?

A. Implement a retry loop that catches JSON parsing errors and re-prompts Claude with the error details, asking it to correct the malformed output.
B. Use regular expressions to locate and extract JSON from the response text, handling cases where Claude includes explanatory text around the JSON block.
C. Define a tool with a JSON schema specifying the expected structure, using tool use to constrain Claude's output to schema-compliant JSON.
D. Add explicit formatting instructions to the prompt with JSON examples, emphasizing that Claude must return only valid JSON with no surrounding text.

問題 #4
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. The agent can reliably extract specific values without parsing free-form text, reducing errors in subsequent operations.
C. Structured JSON is processed deterministically by the model, significantly improving accuracy when extracting values.
D. JSON schemas automatically validate that the underlying API returned correct data before the agent processes it.

問題 #5
Your automated code review is missing genuine bugs in pull requests. Investigation reveals that the review prompt includes this 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 findings are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with every 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. Instruct the model to report all findings with confidence and severity tags, deferring filtering to a downstream step.
B. Remove all severity-related instructions and allow the model to use its default judgment about which findings to report.
C. Enable extended thinking and instruct the model to reason step by step about every code change before producing its review.
D. Add a second review pass that rereads the diff using the same prompt and looks for anything the first pass may have missed.

解説：