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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Claude Code Configuration & Workflows	20%	- Configuration files and structure 1. Custom commands and workflows 2. CLAUDE.md and rules system - CI/CD and development integration 1. Plan mode vs direct execution 2. Pipeline automation and review
Topic 2: Tool Design & MCP Integration	18%	- Tool definition and best practices 1. Tool descriptions and selection logic 2. Error handling and validation - Model Context Protocol (MCP) 1. MCP server and client setup 2. Tools, resources, and prompts integration
Topic 3: Prompt Engineering & Structured Output	20%	- Structured data generation 1. Output validation and reliability 2. JSON schema enforcement - Advanced prompting techniques 1. Few-shot and chain-of-thought prompting 2. System prompts and role framing
Topic 4: Context Management & Responsible AI	15%	- Safety and compliance 1. Refusal handling and risk mitigation 2. Constitutional AI principles - Context window optimization 1. Token management and truncation strategies 2. Context retention and summarization
Topic 5: Agentic Architecture & Orchestration	27%	- Agent design patterns 1. Agent loops and control flow 2. Hub-and-spoke multi-agent systems - Anthropic Agent SDK usage 1. Session state and context handling 2. Task spawning and tool management

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q83-Q88):

NEW QUESTION # 83
When designing a human review workflow with limited reviewer capacity, which of the following items should be prioritized for human review? (Choose 2 correct answers)

A. Extractions where the model reports low field-level confidence.
B. Documents where the tool_choice was set to "auto".
C. Extractions involving ambiguous or contradictory source documents.
D. Randomly selected extractions from documents categorized as having 99% historical accuracy.

Answer: A,C

Explanation:
When reviewer capacity is limited, human review should be prioritized and routed toward items with low model confidence and tasks involving ambiguous, contradictory, or borderline source material where human judgment is strictly required.

NEW QUESTION # 84
In a Continuous Integration (CI) pipeline, you run a script where a single Claude session generates a block of code and then immediately reviews its own changes in the same session. Why is this considered an architectural anti-pattern?

A. It exceeds the maximum token limit for the context window, causing the session to silently truncate the generated code.
B. The Message Batches API does not support multi-turn tool calling within a single request.
C. Same-session reviews trigger rate limiting automatically, breaking the CI/CD pipeline.
D. The reviewer retains the reasoning context from the code generation phase, creating confirmation bias and reducing the likelihood of catching subtle issues.

Answer: D

Explanation:
Same-session self-review is an anti-pattern because the model retains its original reasoning context, creating a blind spot. Independent review instances without prior reasoning context are much more effective at objectively evaluating code.

NEW QUESTION # 85
When passing context between agents in a multi-agent orchestration, why is it highly recommended to use structured data formats?

A. To automatically trigger JSON schema validation by the Claude API.
B. To bypass the maximum token limit of the subagent's isolated context window.
C. To compress the token count of the prompt by 50% using the Message Batches API.
D. To separate content from metadata (such as source URLs, document names, and page numbers) to preserve proper attribution.

Answer: D

Explanation:
Using structured data formats to separate content from metadata (like source URLs, document names, and publication dates) when passing context between agents ensures that downstream synthesis agents preserve proper source attribution and information provenance.

NEW QUESTION # 86
To systematically analyze why developers dismiss certain automated code review findings in a quicktechie.com application, which architectural pattern should be implemented?

A. Run a separate classifier model to predict which findings will be ignored.
B. Execute a multi-pass review on every pull request to ensure consensus.
C. Add a detected_pattern field to the structured findings to track and aggregate dismissal reasons.
D. Use the Message Batches API to delay findings until end-of-week review.

Answer: C

Explanation:
Feedback loop design requires tracking which code constructs trigger specific findings. By including a 'detected_pattern' field in the extraction or review output, teams can systematically analyze pattern clusters when developers dismiss findings, identifying systematic false positives.

NEW QUESTION # 87
In an automated code review workflow, why is it considered a major architectural anti-pattern to have the same Claude session both generate code and subsequently review its own generated changes?

A. The Message Batches API does not support multi-turn tool calling within a single request.
B. The reviewer retains the reasoning context and assumptions from the generation phase, creating confirmation bias.
C. Tools invoked during the first pass are permanently locked and cannot be reused in the review pass.
D. It exceeds the maximum token limits for the context window during the generation phase.

Answer: B

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 catching subtle issues.

NEW QUESTION # 88
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