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Anthropic CCA-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q93-Q98):

NEW QUESTION # 93
A developer wants Claude to consistently answer as an experienced cybersecurity analyst regardless of the user question. Which prompt component should contain this instruction?

A. System prompt
B. Retrieved document
C. User message
D. Assistant response

Answer: A

Explanation:
The system prompt establishes persistent behavioral instructions that apply throughout the conversation. Role definition, response style, and organizational policies belong there because Claude prioritizes system instructions above ordinary user requests whenever possible.

NEW QUESTION # 94
Claude Code provides several built-in tools for codebase exploration and modification. If an agent needs to fix a bug on line 42 of an existing, lengthy TypeScript file, which tool should be utilized to ensure unchanged content is safely preserved?

A. Write, passing the updated content block to rewrite the target file.
B. Bash, using standard Unix utilities like sed to replace the line.
C. Edit, providing the old text block and the new text block for targeted replacement.
D. Glob, searching for the specific line and replacing it via regex.

Answer: C

Explanation:
The Edit tool is purpose-built for making targeted modifications to existing files by finding unique anchor text and replacing it, preserving the rest of the file. Write is an anti-pattern for existing files because it replaces the entire file. Using Bash when a native tool exists is also an anti-pattern.

NEW QUESTION # 95
Why is configuring the exact same Claude session to review its newly generated pull request code considered an architectural anti-pattern?

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

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 # 96
A pull request modifies 14 files across the stock tracking module. A single-pass self-evaluation review analyzing all files together produces inconsistent results: detailed feedback for some files but superficial comments for others. How should you restructure the evaluation architecture?

A. Split into focused passes: analyze each file individually for local issues, then run a separate integration-focused pass examining cross-file data flow.
B. Switch to a higher-tier model with a larger context window to give all 14 files adequate attention in one pass.
C. Require developers to manually split the PR into smaller submissions before the automated review runs.
D. Run three independent review passes on the full PR and only flag issues that appear in at least two of the three runs.

Answer: A

Explanation:
Splitting reviews into focused passes directly addresses the root cause: attention dilution when processing many files at once. File- by-file analysis ensures consistent depth, while a separate integration pass catches cross-file issues. Larger context windows do not solve attention quality issues, and forcing consensus suppresses real bugs.

NEW QUESTION # 97
A customer support agent must never process a refund exceeding $500 without explicit manager approval. What is the most robust and deterministic method to enforce this business rule?

A. Set the tool choice parameter to 'auto' so the model can dynamically assess the financial risk.
B. Provide negative few-shot examples in the prompt showing the agent successfully refusing high-value refunds.
C. Implement a PostToolUse hook that programmatically intercepts the refund tool call and blocks execution if the amount exceeds 500.
D. Include a capitalized warning in the system prompt explicitly forbidding refunds over $500.

Answer: C

Explanation:
For critical business rules requiring deterministic compliance, programmatic enforcement via Agent SDK hooks is required. Prompt- based guidance and few-shot examples are probabilistic and can occasionally be ignored by the LLM. Hooks run as code and provide 100% reliable enforcement.

NEW QUESTION # 98
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