Anthropic CCA-F

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Anthropic CCA-F Exam Syllabus Topics:

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

Question #1
Which prompt revision is MOST likely to improve output quality?

A. "Summarize this."
B. Use only uppercase letters.
C. "Help me."
D. Include objective, context, constraints, and expected format.

Question #2
Your process_refund tool returns two types of errors: technical errors ("503 Service Unavailable",
"Connection timeout") that are transient (5% of calls), and business errors ("Order exceeds
30 day return window", "Item already refunded") that are permanent (12% of calls). Monitoring shows the agent wastes 3-4 turns retrying business errors that can never succeed. Currently, both error types return only a plain text message to Claude. What's the most effective way to reduce wasted retries while improving customer-facing response quality?

A. Return structured error responses with retriable: false for business errors and a customer-friendly explanation for Claude to use.
B. Implement automatic retry logic at the tool level for technical errors only, passing business errors to Claude without retries.
C. Add a check_refund_eligibility tool that must be called before process_refund to prevent business rule violations.
D. Add few-shot examples showing how to distinguish retriable from non-retriable errors by parsing error message text.

Question #3
Your agent has called lookup_order multiple times while investigating a customer's return requests. Each response includes 40+ fields (items, shipping details, payment info, status history). Tool outputs now represent the majority of the conversation's context. The customer mentions two more orders they want to discuss. What's the most effective approach before making additional lookups?

A. Have the model generate a natural language summary of each order's key details, replacing structured responses with prose descriptions
B. Extract only the return-relevant fields (items, purchase date, return window, status) from each existing order response, removing verbose details
C. Proceed with additional lookups without modifying the existing tool output context
D. Move all tool responses to a vector database with semantic indexing, retrieving relevant portions as the conversation continues

Question #4
After the web search agent and document analysis agent complete their tasks, the coordinator invokes the synthesis agent. However, the synthesis agent responds that it cannot complete the task because no research findings were provided. What is the most likely cause of this issue?

A. The subagents need to share a single API connection to enable automatic context sharing between invocations.
B. The synthesis agent needs tools that can fetch results directly from the other agents' conversation histories.
C. The coordinator did not include the outputs from the previous agents in the synthesis agent's prompt.
D. The synthesis agent's context window is not large enough to hold the combined outputs from both previous agents.

Question #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. Increase the initial breadth of queries sent to web search and document analysis to reduce the probability of missing relevant information.
C. Give the synthesis agent direct access to web search tools so it can autonomously fill knowledge gaps without returning control to the coordinator.
D. Have the report generation agent note which research questions couldn't be answered, so users understand the limitations of the final output.

Solutions: