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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Agentic Architecture & Orchestration	27%	- System orchestration patterns 1. Hub-and-spoke agent systems 2. Task decomposition strategies - Agentic loop design and execution lifecycle 1. Tool use decision flow (tool_use vs end_turn) 2. Multi-agent coordination and delegation patterns
Context Management & Reliability	15%	- Long-context optimization 1. Reliability and retry strategies 2. Context window management
Prompt Engineering & Structured Output	20%	- Reliable structured generation 1. Schema-guided outputs 2. Prompt patterns for agents
Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. MCP client/server integration patterns 2. Tool schema design
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 (Q72-Q77):

NEW QUESTION # 72
In a multi-pass review architecture, how can you effectively automate the routing of identified findings for human review calibration?

A. Rely strictly on the model's self-reported conversational confidence score in the output.
B. Run verification passes where the model outputs field-level confidence scores alongside findings to enable calibrated review routing.
C. Escalate any finding that flags more than 5 lines of code.
D. Use the Message Batches API to delay findings until the end-of-week manual review.

Answer: B

Explanation:
To optimize human-in-the-loop workflows, models should output structured, field-level confidence scores that are calibrated against labeled validation sets. This allows reliable routing of low-confidence or high-risk findings for human review.

NEW QUESTION # 73
How should Claude Code be executed in a Continuous Integration (Cl) pipeline to analyze pull requests without hanging indefinitely?

A. Option B
B. Option C
C. Option D
D. Option A

Answer: A

Explanation:
The -p (or -print) flag is strictly required to run Claude Code in non-interactive mode. It processes the prompt, outputs the result to stdout, and exits without waiting for user input, preventing the Cl pipeline from hanging.

NEW QUESTION # 74
A customer sends: "This is frustrating. I've explained my issue twice and nothing is being resolved. I want to talk to a real person NOW." The agent has not yet called any tools to investigate their account. What should the agent do?

A. Acknowledge the frustration and ask one targeted question to understand the specific issue before escalating.
B. First call get_customer and lookup_order to gather account context, then escalate to a human agent.
C. Immediately call escalate_to_human with the conversation history.
D. Briefly explain what the agent can help with and offer to resolve the issue quickly, escalating only if the customer repeats their request.

Answer: C

Explanation:
The customer has made an explicit and urgent request to speak with a human after repeated unresolved attempts. The agent should respect that request immediately and pass the available conversation history so the human agent can continue without forcing the customer to repeat themselves.

NEW QUESTION # 75
Your post_content tool requires user confirmation before publishing. The current workflow displays "Ready to post to social media. Confirm?" and analytics show users approve 98% of requests within 2 seconds. Post-mortems reveal incidents where posts went to wrong accounts, were scheduled for wrong times, or contained errors - all confirmed by users without catching the mistakes. How should you redesign the confirmation workflow?

A. Include the complete post text, target account, scheduled time, and platform in the confirmation request
B. Add a mandatory waiting period before the confirm option becomes available
C. Auto-approve routine posts and only require explicit confirmation for unusual patterns like posting to new accounts or large audiences
D. Require users to type a confirmation phrase instead of clicking a button

Answer: A

Explanation:
A confirmation step must present the specific action details the user is approving. Showing the full post content, target account, scheduled time, and platform helps users detect errors before execution, making confirmation meaningful rather than a generic approval click.

NEW QUESTION # 76
Why is configuring the exact same Claude session to review its newly generated pull request code considered an architectural anti-pattern?

A. The Message Batches API does not support synchronous self-review workflows.
B. Tools invoked during the first pass are permanently locked and cannot be reused in the review pass.
C. The reviewer retains the reasoning context and assumptions from the generation phase, creating confirmation bias.
D. It typically exceeds the maximum token limit of the context window during the generation phase.

Answer: C

Explanation:
Same-session self-review is an anti-pattern because the model retains the context and reasoning from the generation phase, leading to a blind spot or confirmation bias. Independent review instances with fresh contexts are much more effective at objectively evaluating code.

NEW QUESTION # 77
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