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

1. Which statement BEST describes Retrieval-Augmented Generation?

A) It changes token pricing.
B) It permanently retrains Claude.
C) It supplies relevant external knowledge during inference.
D) It compresses model weights.

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

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

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) 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.

4. Your track_shipment(tracking_id) tool queries an external logistics API that sometimes fails - the API may be temporarily unavailable, the tracking ID may be malformed, or the shipment may not exist. Currently, your tool raises a Python exception when errors occur. Users report the agent gives unhelpful responses like "I'm having trouble with that request" instead of suggesting alternatives such as verifying the tracking number format or checking by order number. How should you handle errors in tool results?

A) Create dedicated error-recovery tools (retry_tracking_lookup, search_by_order_number) that the model can invoke after the primary tracking tool returns a failure indicator.
B) Return a generic error response (e.g., {"success": false, "error": "lookup_failed"}) for all failure cases to maintain a consistent schema and avoid exposing internal error details.
C) Implement retry logic with exponential backoff inside the tool implementation so transient errors are automatically handled and only return a result after all retry attempts are exhausted.
D) Return structured error information as normal tool output including error type, recoverability status, and actionable context for the user.

5. Your update_user_profile tool accepts a user_id (required) and an optional fields_to_update object. In testing, Claude frequently omits user_id or passes incorrectly structured data. What is most critical for helping Claude understand what parameter values to provide?

A) Strict JSON Schema type constraints marking user_id as required and defining fields_to_update as an object type
B) Verbose parameter names encoding format hints, such as user_id_string_uuid_format
C) Detailed error responses explaining why invalid parameter values were rejected
D) Clear parameter descriptions explaining expected format, such as "user_id: UUID of the user to update (required)"

Solutions: