Anthropic Claude Certified Architect - Foundations : CCAR-F

明日ではなく、今日が大事と良く知られるから、そんなにぐずぐずしないで早く我々社のClaude Certified Architect - Foundations日本語対策問題集を勉強し、自身を充実させます。我々社の練習問題は長年でClaude Certified Architect - Foundations模擬試験トレーニング資料に研究している専業化チームによって編集されます。Claude Certified Architect - Foundations資格問題集はPDF版、ソフト版、オンライン版を含まれ、この三つバージョンから自分の愛用することを選んでいます。他の人に先立ってCCAR-F認定資格を得るために、今から勉強しましょう。

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Anthropic CCAR-F 試験シラバストピック:

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

1. Production monitoring shows that follow-up queries like "summarize what we learned about market trends" consistently take 40+ seconds. Investigation reveals the coordinator spawns the synthesis subagent for each summarization request, passing 80K+ tokens of accumulated findings. The coordinator already has these findings in its context from orchestrating the research.
What's the most effective way to improve response time for these follow-up summaries?

A) Pre-generate and cache summaries at multiple granularities whenever new findings accumulate.
B) Have the coordinator handle straightforward summarization requests directly using its existing context, reserving subagent spawning for complex analysis.
C) Spawn the synthesis subagent with reduced context and have it request specific findings from the coordinator on-demand.
D) Enable prompt caching on the synthesis subagent to reduce the overhead of repeatedly transferring the same research findings.

2. 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.
After deploying automated code review, developers report that approximately 35% of findings are false positives following consistent patterns: style suggestions that contradict team conventions, security warnings for patterns that are safe in the deployment environment, and performance suggestions that would degrade this particular use case.
You want to reduce false positives while enabling the model to generalize its judgment to novel code patterns it has not seen before.
Which approach is most effective?

A) Include few-shot examples containing annotated code snippets that distinguish acceptable project patterns from genuine issues in each category.
B) Add general instructions telling Claude to be conservative and report only definite issues.
C) Use keyword-based post-processing to remove findings containing terms such as "convention,"
"context-dependent," or "trade-off."
D) Create a comprehensive specification of every pattern that must not be flagged and include the complete document in the system prompt.

3. You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
Your team is configuring MCP servers in Claude Code. You want to add a shared venue lookup server that all team members should have access to, and you personally want to add an experimental music playlist server that only you are testing. Which configuration approach correctly applies MCP server scopes?

A) Add venue server to .mcp.json and playlist server to ~/.claude.json.
B) Add both servers to the project-level .mcp.json file
C) Add both servers to your local ~/.claude.json
D) Add venue server to ~/.claude.json and playlist server to .mcp.json

4. Your pipeline reviews every pull request using a single API call with a static prompt containing the diff and the full text of each changed file; unchanged files are not included. Reviews are posted asynchronously and do not block pull-request creation. Developers report that reviews consistently miss bugs involving cross-file interactions--for example, a pull request renames a function's parameters, but the review does not flag callers in other files that still use the old parameter names. Post-release analysis shows that cross-file bugs account for 35% of production incidents from reviewed pull requests. What is the most effective change to your review design?

A) Add chain-of-thought instructions asking the model to list all external references in the diff and then reason step by step about how each change might affect callers in other files.
B) Use static analysis to build a dependency graph of changed code, and then expand the prompt to include every file within two dependency hops of any changed file.
C) Redesign the review as a turn-limited agentic task in which the model can read files and search the codebase through tools, following references to verify cross-file findings.
D) Run parallel review passes for each changed file with its direct dependents included, and then aggregate and deduplicate the findings through a final summarization call.

5. 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 schema includes a skills: string[] field. Production monitoring reveals three consistency issues:
(1) compound phrases like "Python and SQL" are sometimes kept as one
entry, sometimes split;
(2) implied but unstated skills occasionally appear in extractions;
(3) similar documents produce wildly different array lengths (5-10 vs
40+ entries).
Your prompt currently says "Extract all skills mentioned."
What's the most effective improvement?

A) Add few-shot examples demonstrating compound phrase handling, explicit mention criteria, and appropriate entry granularity.
B) Add constraints: "Extract 10-20 skills maximum, one skill per entry, only explicitly named skills."
C) Add post-extraction normalization that maps skills to a canonical taxonomy and deduplicates similar entries.
D) Enrich the schema to {skill: string, confidence: float, source_quote: string}[] to capture extraction metadata.

質問と回答：