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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prompt Engineering & Structured Output	20%	- Reliable structured generation 1. Prompt patterns for agents 2. Schema-guided outputs
Topic 2: Agentic Architecture & Orchestration	27%	- Agentic loop design and execution lifecycle 1. Multi-agent coordination and delegation patterns 2. Tool use decision flow (tool_use vs end_turn) - System orchestration patterns 1. Hub-and-spoke agent systems 2. Task decomposition strategies
Topic 3: Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. MCP client/server integration patterns 2. Tool schema design
Topic 4: Context Management & Reliability	15%	- Long-context optimization 1. Context window management 2. Reliability and retry strategies
Topic 5: Claude Code Workflows & Configuration	20%	- Claude Code operational patterns 1. Plan mode vs execution mode 2. CI/CD and workflow integration

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q36-Q41):

NEW QUESTION # 36
A customer submits a single request containing three distinct, complex issues (e.g., a missing item, a billing dispute, and a technical bug). How should the agentic workflow handle this multi-concern request?

A. Decompose the request into distinct items, investigate each in parallel using shared context, and synthesize a unified resolution.
B. Politely request the user to submit three separate support tickets.
C. Process the first issue identified and ignore the others to maintain workflow focus.
D. Escalate the conversation to a human immediately to avoid exceeding capability limits.

Answer: A

Explanation:
Best practices for multi-concern customer requests involve decomposing the overarching request into distinct sub-items, investigating each concern in parallel (potentially via subagents) using shared context, and finally synthesizing a unified resolution for the customer.

NEW QUESTION # 37
When designing an automated code review system for a large repository, you notice that developers are experiencing 'alert fatigue' because the AI flags too many non-issues and stylistic preferences. What is the most effective prompt engineering technique to reduce these false positives?

A. Provide at least 15 few-shot examples covering every possible coding style in the repository.
B. Set the model's temperature to 0.0 to ensure deterministic output and eliminate all false positives.
C. Define explicit, measurable review criteria, such as flag functions exceeding 50 lines of code, instead of relying on vague instructions.
D. Instruct the model to be thorough and flag everything suspicious to ensure no bugs are missed.

Answer: C

Explanation:
Explicit, measurable criteria produce consistent, actionable results that build developer trust and reduce false positives. Vague instructions like 'be thorough' lead to over-flagging and alert fatigue. Temperature adjustments do not fix vague criteria, and providing 15 few-shot examples bloats the prompt without proportional benefit.

NEW QUESTION # 38
A quicktechie.com development team wants to run a comprehensive codebase audit analyzing technical debt every weekend. Which API approach is most architecturally sound and cost-effective?

A. A single, long-running extended session using the- -resume flag.
B. The Message Batches API.
C. Multi-agent orchestration running locally.
D. Synchronous API calls executed in parallel.

Answer: B

Explanation:
The Message Batches API is ideal for non-urgent, latency-tolerant workloads like overnight or weekend technical debt audits. It processes requests asynchronously within a 24-hour window and offers a 50% cost savings over synchronous calls.

NEW QUESTION # 39
To integrate an MCP server providing database tools for a team, you must configure it so that all developers share the integration but do not leak secrets into version control. What is the correct configuration approach?

A. Hardcode an encrypted API token directly in the .mcp.json file.
B. Configure the server in the user-scoped ~/. claude. json file to keep credentials local.
C. Store the credentials in an ignored secrets. json file referenced by CLAUDE. md.
D. Configure the server in the project-scoped .mcp. j son file using environment variable expansion like ${DB_TOKEN}.

Answer: D

Explanation:
Shared team MCP integrations belong in the project-level .mcp.json file. To prevent credential leaks in version control, you must use environment variable expansion (e.g., ${DB_TOKEN}) so secrets remain in the local environment and not in the code.

NEW QUESTION # 40
During a data extraction task, a validation check fails because the extracted line items do not sum to the total. How should the follow-up retry request be structured for the best results?

A. Include the original document, the failed extraction, and the specific validation error details.
B. Return a generic error message: There were errors. Please try again.
C. Automatically increase the model temperature and resubmit the original prompt.
D. Use a PreToolUse hook to normalize the document structure.

Answer: A

Explanation:
A successful validation-retry loop requires appending specific error details (e.g., 'Line items sum doesn't match subtotal') to the prompt and supplying both the source document and the failed extraction. Generic retry messages provide no correction signal.

NEW QUESTION # 41
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