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最新的 Claude Certified Architect CCAR-F 免費考試真題：

1. 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) 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.
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) 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.
D) 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.

2. Your multi-agent research pipeline crashed after processing12 of 28 documents. The web search agent had identified relevant sources, the document analyzer had partially completed extraction, and the synthesizer had begun pattern identification. You need to resume processing without repeating work or losing fidelity of prior findings. What state management approach best balances information fidelity with context efficiency when restoring agent state?

A) Index all agent outputs in a shared vector store. When resuming, each agent queries the store using semantic search to retrieve relevant prior findings.
B) Persist the coordinator's conversation log containing all task delegations and responses, providing this to agents when resuming.
C) Have each agent persist a structured export to a known location. On resume, the coordinator loads the manifest and injects relevant state into agent prompts.
D) Have each agent maintain its own persistent state file and reload it independently at the start of each session.

3. Your infrastructure-as-code repository includes Terraform modules (/terraform/), Kubernetes manifests (/kubernetes/), and CI/CD pipeline scripts (/pipelines/). Each requires different conventions, but your single root CLAUDE.md has grown to 500+ lines. When developers work on Kubernetes files, Terraform-specific rules load into context unnecessarily, consuming tokens.
What is the best approach to reorganize so only relevant guidance loads when editing specific file types?

A) Split content into subdirectory CLAUDE.md files (/terraform/CLAUDE.md,
/kubernetes/CLAUDE.md), so Claude loads directory-specific guidance.
B) Create files in .claude/rules/ with YAML frontmatter path-scoping (e.g., paths: ["terraform/**/*"]), loading rules only when editing matching files.
C) Restructure the root CLAUDE.md into clearly labeled sections with headers (e.g, "## Terraform Conventions"), improving organization and readability.
D) Keep the root CLAUDE.md and use @path/to/import syntax to modularly include tool-specific guidance files from separate documents.

4. Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document analysis agent frequently identifies gaps - for instance, noting "the retrieved sources discuss API authentication but lack details on token refresh patterns" - but under the current strict pipeline, this insight isn't actionable since search has already completed. What's the most effective architectural change?

A) Have the analysis agent report specific gaps to the coordinator, which triggers targeted searches and re-invokes analysis until sufficient.
B) Add a research planning agent before the search phase that decomposes topics into specific sub- questions.
C) Have the synthesis agent attach confidence scores to each section and flag areas with insufficient coverage for manual review.
D) Have the coordinator review analysis output for gap indicators and re-invoke search with gap- informed queries when gaps are detected.

5. You built an LLM-powered code-review tool that analyzes pull requests and returns structured findings. Each finding is a JSON object containing file_path, line_number, issue_category--such as security or style--and description. Developers can dismiss findings they consider unhelpful, and currently 35% of findings are dismissed. You want to analyze these dismissals to understand what the system is getting wrong and improve the prompts accordingly. What change to the output structure would best support this analysis?

A) Add a detected_pattern field recording the specific code construct that triggered the finding, such as single-letter loop variable.
B) Remove the issue_category field and track dismissal rates only at the individual-finding level.
C) Expand the description field with more detailed explanations of why each issue matters and how it should be fixed.
D) Add a model_confidence field from 0.0 to 1.0 and filter findings below a threshold calibrated against historical dismissal rates.

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