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Anthropic CCA-F Exam Syllabus Topics:

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

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.
A critical bug is affecting production users. Error logs show exceptions in the OrderProcessing module with a clear stack trace pointing to a specific function. You haven't worked with this module before. What's the most effective approach?

A) Start with direct execution to gather initial information, then switch to plan mode to design a comprehensive solution before implementing any changes.
B) Use direct execution to examine the stack trace, read the relevant code, and implement a fix once you identify the root cause.
C) Use plan mode to analyze the error in context of the module's design, enumerate potential root causes, and prioritize fixes systematically.
D) Enter plan mode to explore the module's architecture and dependencies before attempting any fixes.

2. What is the PRIMARY purpose of few-shot prompting?

A) Demonstrate desired output patterns.
B) Increase model size.
C) Encrypt prompts.
D) Reduce token costs.

3. Your document extraction tool uses ML models to extract invoice fields (vendor, amount, date).
The models return confidence scores (0.0-1.0) for each extracted field. In production, you observe: (1) the agent proceeds with low-confidence extractions that are incorrect 23% of the time, and (2) the agent requests unnecessary human review for 31% of extractions that were actually correct. How should you restructure the tool's output?

A) Return fields organized into verified and needs_verification objects based on confidence thresholds.
B) Return fields with their raw confidence scores and add detailed few-shot examples to your system prompt demonstrating how to interpret different confidence ranges and when to request human review.
C) Compute an aggregate extraction_quality score across all fields and return it alongside the extracted values. Include a text summary describing the overall extraction reliability.
D) Return fields with confidence scores, plus a request_review boolean computed using your tested confidence thresholds, along with a review_reasons array explaining which fields triggered review.

4. 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) Auto-approve routine posts and only require explicit confirmation for unusual patterns like posting to new accounts or large audiences
C) Add a mandatory waiting period before the confirm option becomes available
D) Include the complete post text, target account, scheduled time, and platform in the confirmation request

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

Solutions: