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

Your post_content tool requires user confirmation before publishing. The current workflow displays "Ready to post to social media. Confirm?" and analytics show users approve 98% of requests within 2 seconds. Post-mortems reveal incidents where posts went to wrong accounts, were scheduled for wrong times, or contained errors - all confirmed by users without catching the mistakes. How should you redesign the confirmation workflow?

• A. Require users to type a confirmation phrase instead of clicking a button
• B. Add a mandatory waiting period before the confirm option becomes available
• C. Auto-approve routine posts and only require explicit confirmation for unusual patterns like posting to new accounts or large audiences
• D. Include the complete post text, target account, scheduled time, and platform in the confirmation request

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

The document analysis agent has a single analyze_documnet tool that takes a document and a free-text instruction parameter. During evaluation, requests like "extract the key financial metrics" often return narrative summaries, while "summarize the methodology" sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require re-requests with clarified instructions. What's the most effective way to improve reliability?

• A. Enhance the tool description with detailed examples showing how different instruction phrasings should map to different output formats
• B. Have the coordinator pre-classify each analysis request before passing instructions to the document analysis agent
• C. Split the generic tool into purpose-specific tools - extract_data_points, summarize_content, verify_claim_against_source - each with defined input/output contracts
• D. Keep the single tool but add an analyze_type enum parameter requiring explicit selection between extraction, summarization, and verification modes

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 (e.g., "What year was the Paris Climate Agreement signed?") traverse all four subagents sequentially, consuming 40+ seconds and significant tokens per query. Complex comparative research benefits from the full pipeline.
Your query distribution is diverse and evolving as users discover new applications. What's the most effective approach to optimize for varying query complexity?

• A. Create a fast-path for factual questions that bypasses subagents entirely, routing all other queries through the complete pipeline to ensure research thoroughness.
• B. Have the coordinator analyze each query and dynamically decide which subagents to invoke based on its assessment of query requirements.
• C. Implement pattern-based routing that categorizes queries by structure (single-fact vs.
  comparative vs. analytical) and maps each category to a predefined subagent combination.
• D. Train a query complexity classifier on labeled historical data to predict optimal subagent combinations, retraining periodically as query patterns evolve.

A prompt asks Claude to produce Markdown tables. Which addition MOST increases consistency?

• A. Increase randomness.
• B. Omit examples.
• C. Specify exact column names.
• D. Remove formatting instructions.