Anthropic Claude Certified Architect Foundations (CCA-F) : CCA-F

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Anthropic Claude Certified Architect Foundations (CCA-F) CCA-F Prüfungsfragen mit Lösungen

Frage #1

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's CLAUDE.md includes a rule: "Use 4-space indentation and always run Prettier formatting." Despite this, code reviews reveal that roughly 30% of files Claude Code generates use inconsistent formatting - sometimes 2-space indentation, sometimes missing trailing commas. Adding emphasis ("IMPORTANT: You MUST use Prettier formatting") reduces violations to about 15%, but doesn't eliminate them. What is the most effective way to ensure all generated code is consistently formatted?

A. Add a Stop hook with a prompt-based check that evaluates whether generated code follows formatting standards and prompts Claude to fix violations.
B. Extract the formatting rules into a dedicated skill that Claude loads automatically when generating code, with more detailed examples of correct formatting.
C. Split the formatting rules into path-scoped .claude/rules/files that load when Claude works on matching file types.
D. Configure a PostToolUse hook with an Edit|Write matcher that automatically runs Prettier on each file Claude modifies.

Frage #2

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

Frage #3

An architect needs Claude to produce structured JSON that downstream software can parse automatically. Which prompting strategy is BEST?

A. Increase maximum output tokens only.
B. Explicitly specify the required JSON schema and formatting rules.
C. Use multiple unrelated examples.
D. Ask Claude to "respond normally."

Frage #4

Your content curation agent discovers articles, analyzes each for relevance, then adds selected articles to themed collections. With separate discover_articles(topic), analyze_article(id), and add_to_collection(article_id, collection_id) tools, you observe 18+ sequential tool calls per request, causing latency issues. The agent must make editorial judgments about which articles fit a collection's theme - this requires seeing all candidates with their analysis scores simultaneously to select a cohesive set. What tool composition best addresses efficiency while preserving editorial judgment?

A. Keep all tools separate but implement response caching for analyze_article calls.
B. Create a discover_and_analyze(topic) composite tool that returns all candidates with their analysis scores, keeping add_to_collection separate for selective calls.
C. Create a curate_collection(topic, collection_id) tool that handles discovery, analysis, and selection internally using configurable quality thresholds.
D. Add a preview_curation(topic, collection_id) tool that shows what would be added based on predefined rules, with an approve_curation() tool to confirm.

Frage #5

Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true, "content": [{"type": "text", "text": "Operation failed"}]}. The agent cannot distinguish between error types. What's the most effective improvement?

A. Enhance error responses with structured metadata: include errorCategory (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.
B. Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.
C. Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.
D. Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.

Fragen und Antworten: