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Anthropic CCA-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q42-Q47):

NEW QUESTION # 42
A developer at quicktechie.com wants to configure Claude Code to always use their preferred Vim keybindings, without enforcing this standard on the rest of their team. Where must this configuration be placed?

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

Answer: B

Explanation:
Personal preferences (like editor settings or Vim keybindings) must go in the user-level configuration located at ~/.claude/CLAUDE.md. Placing them in the project-level .claude/CLAUDE.md would share them via version control and force them onto the entire team, which is an anti-pattern.

NEW QUESTION # 43
When applying few-shot prompting to improve output consistency for a task with ambiguous boundaries, what is the optimal number of examples to provide?

A. 8 to 10 examples.
B. 2 to 4 examples.
C. 15 or more examples.
D. 1 example.

Answer: B

Explanation:
The golden rule of few-shot prompting is to provide 2-4 examples. Fewer than 2 does not firmly establish a pattern, while providing more than 6 examples generally bloats the prompt without adding proportional benefit.

NEW QUESTION # 44
Which statement BEST describes Retrieval-Augmented Generation?

A. It supplies relevant external knowledge during inference.
B. It compresses model weights.
C. It permanently retrains Claude.
D. It changes token pricing.

Answer: A

Explanation:
RAG retrieves relevant documents at inference time and includes them within the prompt. The model itself is unchanged, allowing organizations to incorporate current enterprise knowledge without retraining foundation models.

NEW QUESTION # 45
The synthesis agent receives summarized findings from the web search and document analysis agents, then passes a consolidated summary to the report generator. During testing, you discover the generated reports make factual claims without proper citations - the report generator cannot attribute statements to their original sources because that metadata was lost during the summarization steps. What's the most effective approach to ensure proper source attribution in the final reports?

A. Skip summarization and pass full raw outputs from web search and document analysis directly to the report generator.
B. Have the report generator query the web search agent to re-locate sources for claims in the final report.
C. Instruct the synthesis agent to embed source references inline within its summary text using a consistent citation format.
D. Have each agent output structured data separating content summaries from source metadata (URLs, document names, page numbers).

Answer: D

Explanation:
Structured outputs that keep summaries separate from source metadata preserve attribution throughout the agent workflow. This allows the synthesis and report generation stages to carry forward URLs, document names, page numbers, and other citation details without losing the link between claims and their original sources.

NEW QUESTION # 46
Which of the following strategies are explicitly recommended for designing multi-instance and multi-pass review architectures? Choose 2 correct answers.

A. Having the same session review its newly generated code to preserve maximum architectural context.
B. Forcing consensus across 3 monolithic runs to suppress false positives in large multi-file PRs.
C. Splitting multi-file reviews into focused per-file passes for local issues plus a separate cross-file integration pass.
D. Using an independent Claude instance to review generated code without the generator's reasoning context.

Answer: C,D

Explanation:
Independent review instances without prior reasoning context effectively catch subtle issues without confirmation bias. Splitting reviews into focused per-file passes and integration passes addresses attention dilution. Same-session reviews and monolithic consensus runs are anti-patterns.

NEW QUESTION # 47
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