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Anthropic CCA-F Exam Syllabus Topics:

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

1. Your control_device tool manages smart home devices through external APIs. When a device doesn't respond within the timeout period, the tool returns an error. Production logs show that the agent simply tells users "the device is not responding" without offering helpful next steps. Which error response structure would best enable the agent to provide useful follow-up?

A) Set is_error: false with an optimistic message indicating the command was dispatched successfully but device acknowledgment is still pending.
B) Set is_error: true with a brief "Device offline" message and provide a separate tool the agent can call to retrieve context-specific troubleshooting suggestions.
C) Set is_error: true with a structured technical error containing the device ID, timeout duration, and raw API response code for debugging purposes.
D) Set is_error: true with a message explaining the likely cause and suggesting troubleshooting steps the agent can offer the user.

2. 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 frequently migrates React components to Vue. You've written a step-by-step workflow for Claude Code to follow during each migration, and you want every developer on the team to invoke it by typing /migrate-component. The workflow should stay in sync as the team iterates on it. Where should you place the skill file?

A) In .claude/skills/migrate-component/SKILL.md at the project root, committed to version control
B) As a detailed instruction block in the project's root CLAUDE.md file
C) In the project's.claude/settings.json using a skillOverrides entry to register and define the workflow
D) In ~/.claude/skills/migrate-component/SKILL.md on each developer's machine

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

4. 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) Enable prompt caching on the synthesis subagent to reduce the overhead of repeatedly transferring the same research findings.
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) Pre-generate and cache summaries at multiple granularities whenever new findings accumulate.

5. The synthesis agent completes its initial pass but flags that three key research questions remain unanswered because the web search and document analysis agents didn't find relevant information on those specific subtopics. The coordinator currently proceeds directly to report generation, producing reports with incomplete coverage. What change would most effectively improve research completeness?

A) Have the coordinator evaluate synthesis output for gaps, then re-delegate to web search and document analysis with targeted queries before invoking synthesis again.
B) Have the report generation agent note which research questions couldn't be answered, so users understand the limitations of the final output.
C) Increase the initial breadth of queries sent to web search and document analysis to reduce the probability of missing relevant information.
D) Give the synthesis agent direct access to web search tools so it can autonomously fill knowledge gaps without returning control to the coordinator.

Solutions: