最新のCCAR-F問題集で試験を準備する

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

問題 #1

A financial services company plans to integrate Claude into an internal document analysis platform. The architects want to minimize exposure of confidential client information while maintaining high-quality responses. Which approach BEST aligns with Anthropic's recommended architecture?

A. Send every document without preprocessing to maximize context.
B. Replace Claude with a smaller local model regardless of task complexity.
C. Store every prompt permanently for auditing.
D. Remove unnecessary sensitive information before sending prompts whenever possible.

問題 #2

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your pipeline uses a tool called extract_metadata with a JSON schema for paper details. You've also defined lookup_citations and verify_doi tools for enrichment. During testing, you notice that when users include requests like "extract the metadata and tell me how cited it is," Claude sometimes calls lookup_citations first, which fails because it needs the DOI that extract_metadata would provide.
What's the most effective way to ensure structured metadata extraction happens first?

A. Set tool_choice to {"type": "tool", "name": "extract_metadata"} and process the enrichment requests in subsequent turns after receiving the extracted metadata.
B. Set tool_choice to "any" so Claude must use a tool, combined with system prompt instructions prioritizing extract_metadata.
C. Set tool_choice to "auto" and reorder the tool definitions so extract_metadata appears first in the tools array, since Claude prioritizes earlier-listed tools.
D. Set tool_choice to {"type": "tool", "name": "extract_metadata"} for every API call in the pipeline, ensuring Claude always extracts metadata before any enrichment can occur.

問題 #3

Your track_shipment(tracking_id) tool queries an external logistics API that sometimes fails - the API may be temporarily unavailable, the tracking ID may be malformed, or the shipment may not exist. Currently, your tool raises a Python exception when errors occur. Users report the agent gives unhelpful responses like "I'm having trouble with that request" instead of suggesting alternatives such as verifying the tracking number format or checking by order number. How should you handle errors in tool results?

A. Return a generic error response (e.g., {"success": false, "error": "lookup_failed"}) for all failure cases to maintain a consistent schema and avoid exposing internal error details.
B. Create dedicated error-recovery tools (retry_tracking_lookup, search_by_order_number) that the model can invoke after the primary tracking tool returns a failure indicator.
C. Implement retry logic with exponential backoff inside the tool implementation so transient errors are automatically handled and only return a result after all retry attempts are exhausted.
D. Return structured error information as normal tool output including error type, recoverability status, and actionable context for the user.

問題 #4

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.
In production, you observe that simple fact-checking queries, such as "In what year was the Paris Climate Agreement signed?", traverse all four subagents sequentially, consuming more than 40 seconds and significant tokens per query. Complex comparative research benefits from the complete pipeline. Your query distribution is diverse and continues to evolve as users discover new applications.
What is the most effective approach to optimize for varying query complexity?

A. Implement pattern-based routing that classifies queries as single-fact, comparative, or analytical and maps each category to a predefined subagent combination.
B. Have the coordinator analyze each query and dynamically determine which subagents are required.
C. Create a fast path for factual questions that bypasses subagents entirely, routing every other query through the complete pipeline.
D. Train a query-complexity classifier using labeled historical data to predict the optimal subagent combination, retraining it periodically.

問題 #5

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's exploration subagent spent 30 minutes analyzing a legacy payment system, reading 47 files and documenting data flows. The session was interrupted when the engineer's connection dropped. While away, a teammate merged a PR that renamed two utility functions.
The engineer wants to continue the same exploration.
What's the most effective approach?

A. Resume the subagent from its previous transcript and inform it about the renamed functions.
B. Resume the subagent from its previous transcript without mentioning the changes--the architecture understanding remains valid.
C. Launch a fresh subagent and include the prior transcript in the initial prompt for context.
D. Launch a fresh subagent with a summary of prior findings.

解説：