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Anthropic CCA-F Exam Syllabus Topics:

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

1. 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) Have the coordinator handle straightforward summarization requests directly using its existing context, reserving subagent spawning for complex analysis.
B) Enable prompt caching on the synthesis subagent to reduce the overhead of repeatedly transferring the same research findings.
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.

2. The synthesis agent receives summarized findings from the web search and document analysis agents, then passes a consolidated summary to the report generator. During testing, you discover the generated reports make factual claims without proper citations - the report generator cannot attribute statements to their original sources because that metadata was lost during the summarization steps. What's the most effective approach to ensure proper source attribution in the final reports?

A) Have the report generator query the web search agent to re-locate sources for claims in the final report.
B) Have each agent output structured data separating content summaries from source metadata (URLs, document names, page numbers).
C) Skip summarization and pass full raw outputs from web search and document analysis directly to the report generator.
D) Instruct the synthesis agent to embed source references inline within its summary text using a consistent citation format.

3. A customer returns 4 hours after the initial session about the same billing dispute. The previous
32-turn session contains lookup_order results showing "Status: PENDING, Expected resolution:
24-48 hours." In testing, you observe that when resuming sessions with stale tool results, the agent often references the outdated data in responses (e.g., "I see your refund is still being processed") even after subsequent fresh tool calls return different information. What approach most reliably handles returning customers?

A) Resume with full history and add a system prompt instruction telling the agent to always prefer the most recent tool results when multiple calls to the same tool exist in context.
B) Resume with full history and configure the agent to automatically re-call all previously-used tools at session start to ensure data freshness.
C) Resume with full history but filter out previous tool_result messages before resuming, keeping only the human/assistant turns so the agent must re-fetch needed data.
D) Start a new session, inject a structured summary of the previous interaction (issue type, actions taken, resolution status), then make fresh tool calls before engaging.

4. When implementing your lookup_order MCP tool, the backend sometimes returns errors (e.g.,
"Order not found" or temporary database failures). What is the correct pattern for communicating these errors back to the agent?

A) Log the error server-side and return an empty result to avoid confusing the model
B) Return the error message in the tool result content with the isError flag set to true
C) Return a success response with a "status" field indicating the error type
D) Throw an exception from the tool handler so the agent framework can catch and log it

5. Which statement BEST describes Retrieval-Augmented Generation?

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

Solutions: