Anthropic : CCA-F Questions & Answers
Last Updated: Sep 10, 2026
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Anthropic CCA-F Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Structured data generation
|
| Topic 2: Claude Code Configuration & Workflows | 20% | - Configuration files and structure
|
| Topic 3: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP)
|
| Topic 4: Context Management & Responsible AI | 15% | - Safety and compliance
|
| Topic 5: Agentic Architecture & Orchestration | 27% | - Anthropic Agent SDK usage
|
Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When analyzing complex legal cases that cite multiple precedents, the document analysis subagent processes each sequentially. A landmark case citing 12 precedents takes over 3 minutes to analyze completely. What's the most effective way to reduce this latency while preserving the coordinator's ability to monitor and debug the system?
- A. Enable the document analysis subagent to spawn its own specialized subagents dynamically when it encounters cases with many citations
- B. Implement a message queue where precedent analysis tasks are processed asynchronously by a pool of worker agents
- C. Have the coordinator spawn parallel document analysis subagents, each handling a subset of precedents, then aggregate results before synthesis
- D. Create a recursive agent hierarchy where analysis agents subdivide work among child agents until reaching single-precedent granularity
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You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth - having the agent propose specific code changes for each - before deciding which to implement. What's the most effective way to structure this exploration?
- A. Start two fresh sessions, manually providing a summary of yesterday's analysis findings to establish context.
- B. Use fork_session to create two branches from yesterday's analysis, exploring one approach in each fork.
- C. Resume yesterday's session and explore both approaches sequentially within the same conversation thread.
- D. Resume yesterday's session to explore the first approach, then start a new session for the second, manually recreating the original context.
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During a billing dispute resolution, your agent successfully retrieves customer info via get_customer and order details via lookup_order, but when attempting to call process_refund, the tool returns a timeout error. The agent has enough information to explain the charges and verify refund eligibility, but cannot actually process the refund due to the backend failure. What approach best balances first-contact resolution with appropriate error handling?
- A. Explain the billing confirm refund eligibility, acknowledge the system issue preventing immediate processing, and offer escalation or retry later
- B. Confirm the refund will be processed and close the conversation, since the system has all necessary information to complete it automatically
- C. Implement automatic retries with exponential backoff for process_refund, keeping the conversation open until the refund is successfully processed
- D. Escalate immediately to a human agent since the refund action cannot be completed
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An engineering team notices that Claude occasionally provides answers beyond the company's internal policy documents. They want responses to rely only on approved documentation whenever possible. Which solution is MOST appropriate?
- A. Shorten every prompt
- B. Disable system prompts
- C. Increase temperature to 1.0
- D. Use Retrieval-Augmented Generation (RAG)
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You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When researching "renewable energy adoption," the web search agent returns recent statistics (2024: 35% adoption) while the document analysis agent extracts data from internal reports (2021: 18% adoption). The synthesis agent incorrectly flags these as contradictory sources rather than recognizing the data shows growth over time. What change would best enable the synthesis agent to correctly interpret such temporal differences?
- A. Require subagents to include publication or data collection dates in their structured outputs.
- B. Instruct the synthesis agent to always treat the most recent data as authoritative and place older findings in a separate historical appendix.
- C. Add a conflict resolution agent that automatically discards older data when newer data exists for the same metric.
- D. Configure the web search agent to only return results from the past 6 months.
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