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Anthropic CCA-F Exam Syllabus Topics:

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

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

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

2. Your product search tool queries an external catalog API and returns matching items. In production, you observe the agent frequently retries searches immediately after receiving zero results, treating "no matches found" as a failure requiring retry. The external API returns HTTP 200 with an empty results array - a valid response. How should you restructure the tool's result to help the agent correctly interpret empty result sets?

A) Return a natural language string describing the outcome, allowing the agent to interpret the result contextually based on the message content.
B) Add a suggestions field containing alternative search strategies when results are empty, helping guide the agent toward more productive follow-up queries.
C) Return a result object with isError: true and a message explaining no products matched.
D) Return a structured result with a success boolean and results array, reserving isError: true for actual execution failures only.

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

4. The coordinator provides detailed step-by-step instructions to the web search subagent, specifying exact search queries, source priorities, and date filters. Production monitoring reveals three issues: (1) the subagent reports "insufficient results" rather than trying alternative approaches when pre-specified searches fail, (2) research quality drops for emerging topics that don't match expected patterns, and (3) the subagent rarely surfaces valuable tangential sources.
What's the most effective way to improve subagent adaptability?

A) Implement a topic classification step where the coordinator categorizes requests as "well-defined" or "exploratory" and uses different instruction styles for each category.
B) Specify research goals and quality criteria (coverage breadth, source diversity, recency) rather than procedural steps, letting the subagent determine its search strategy.
C) Add explicit fallback directives to the detailed instructions: "If specified searches yield fewer than N results, attempt alternative query formulations before reporting failure."
D) Remove procedural details entirely, delegating with simple goals like "research X thoroughly" and relying on the subagent's general capabilities.

5. Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true, "content": [{"type": "text", "text": "Operation failed"}]}. The agent cannot distinguish between error types. What's the most effective improvement?

A) Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.
B) Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.
C) Enhance error responses with structured metadata: include errorCategory (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.
D) Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.

Solutions: