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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Context Management & Reliability	15%	- Long-context optimization 1. Reliability and retry strategies 2. Context window management
Claude Code Workflows & Configuration	20%	- Claude Code operational patterns 1. Plan mode vs execution mode 2. CI/CD and workflow integration
Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. Tool schema design 2. MCP client/server integration patterns
Prompt Engineering & Structured Output	20%	- Reliable structured generation 1. Prompt patterns for agents 2. Schema-guided outputs
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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q27-Q32):

NEW QUESTION # 27
When designing an extraction system for documents with highly varied structures (e.g., inline citations versus bibliographies), what is the most effective prompt engineering technique to achieve consistent output?

A. Provide few-shot examples demonstrating correct extraction from documents with varied formats.
B. Implement a PreToolUse hook to normalize the document structure before it reaches the context window.
C. Use dynamic adaptive decomposition to spawn a subagent for each distinct document type.
D. Rely exclusively on a strict JSON schema and avoid providing any examples to prevent overfitting.

Answer: A

Explanation:
Few-shot examples are highly effective for demonstrating correct handling of varied document structures, such as inline citations versus bibliographies or narrative descriptions versus structured tables.

NEW QUESTION # 28
To optimize a structured data extraction pipeline, you want to route only the most uncertain documents to your limited pool of human reviewers. Which method provides the most reliable calibration for this routing?

A. Having the model output field-level confidence scores, and calibrating those thresholds using a labeled validation set.
B. Relying on the model's self-reported confidence score in the conversational text output.
C. Routing any document that triggers a validation-retry loop more than once.
D. Escalating documents based solely on the aggregate accuracy metric of the entire pipeline.

Answer: A

Explanation:
For effective human-in-the-loop routing, models should output structured, field-level confidence scores that are then calibrated against labeled validation sets. Self-reported conversational confidence is unreliable, and aggregate metrics mask specific field-level or document- type failures.

NEW QUESTION # 29
To keep the main agent's context clean while investigating a complex authentication flow, which architectural pattern is highly recommended?

A. Use the Message Batches API to process the exploration synchronously.
B. Spawn a subagent to isolate the verbose exploration output, returning only a focused summary to the main agent.
C. Use the fork_session command to duplicate the main agent permanently.
D. Apply progressive summarization to the main conversation history every 2 turns.

Answer: B

Explanation:
Subagent delegation is the recommended pattern to isolate verbose exploration output (like reading multiple files and tracing functions) from the main coordinator. The subagent does the heavy lifting in its own isolated context and returns only a clean synthesis to the main agent.

NEW QUESTION # 30
A company wants Claude to summarize thousands of support tickets efficiently. Which design scales BEST?

A. Remove system prompts.
B. Increase temperature.
C. Send all tickets in one request.
D. Process tickets individually and combine summaries.

Answer: D

Explanation:
Hierarchical summarization is an effective strategy for large datasets. Claude summarizes smaller batches first, then combines intermediate summaries into higher-level reports, improving scalability while maintaining important information.

NEW QUESTION # 31
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
Your team frequently migrates React components to Vue. You've written a step-by-step workflow for Claude Code to follow during each migration, and you want every developer on the team to invoke it by typing /migrate-component. The workflow should stay in sync as the team iterates on it. Where should you place the skill file?

A. In the project's.claude/settings.json using a skillOverrides entry to register and define the workflow
B. As a detailed instruction block in the project's root CLAUDE.md file
C. In ~/.claude/skills/migrate-component/SKILL.md on each developer's machine
D. In .claude/skills/migrate-component/SKILL.md at the project root, committed to version control

Answer: D

Explanation:
A project-scoped skill is available to everyone working in that repository and stays synchronized through version control. The skill directory name exposes it as /migrate-component.

NEW QUESTION # 32
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