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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. Tool schema design 2. MCP client/server integration patterns
Topic 2: Claude Code Workflows & Configuration	20%	- Claude Code operational patterns 1. Plan mode vs execution mode 2. CI/CD and workflow integration
Topic 3: Prompt Engineering & Structured Output	20%	- Reliable structured generation 1. Prompt patterns for agents 2. Schema-guided outputs
Topic 4: Agentic Architecture & Orchestration	27%	- System orchestration patterns 1. Task decomposition strategies 2. Hub-and-spoke agent systems - Agentic loop design and execution lifecycle 1. Multi-agent coordination and delegation patterns 2. Tool use decision flow (tool_use vs end_turn)
Topic 5: Context Management & Reliability	15%	- Long-context optimization 1. Context window management 2. Reliability and retry strategies

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q100-Q105):

NEW QUESTION # 100
An automated pipeline is using a PreToolUse hook to block unauthorized file reads by evaluating the requested file path. How does the hook command properly communicate to Claude *why* the action was blocked so the agent can adjust its behavior?

A. By returning an empty JSON array [] on standard output.
B. By returning an exit code of 2 and writing the explanation to standard error, which is fed back to Claude as feedback.
C. By overriding the context window entirely with a generic access denied template.
D. By silently terminating the session and initiating a new one with an updated system prompt.

Answer: B

Explanation:
In Claude Code hooks, an exit code of 2 in a PreToolUse hook blocks the tool call. Any message written to standard error (stderr) is fed back directly to Claude as feedback, explaining exactly why the operation was blocked so the model can adapt.

NEW QUESTION # 101
Which statement BEST describes Retrieval-Augmented Generation?

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

Answer: C

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 # 102
When re-running an automated PR review after new commits, what is the best practice to avoid posting duplicate comments on existing issues?

A. Run grep via Bash to parse previous GitHub comments and filter them out programmatically.
B. Include prior review findings in the context and instruct Claude to report only new or still-unaddressed issues.
C. Clear the context window completely using /clear before every run.
D. Use the Message Batches API to automatically deduplicate comments across runs.

Answer: B

Explanation:
To avoid duplicate comments, best practice dictates including prior review findings in the context when re-running reviews after new commits, explicitly instructing Claude to report only new or unaddressed issues.

NEW QUESTION # 103
You are tasked with using Claude Code to add comprehensive unit tests to an unfamiliar legacy codebase at quicktechie.com. Which approach represents the recommended decomposition strategy for this open-ended task?

A. Request Claude to automatically generate a monolithic test suite in a single prompt execution.
B. Use a fixed prompt chaining pipeline that executes npm test after generating each line of code.
C. First map the project structure, identify high-impact areas, and create a prioritized plan that adapts as dependencies are discovered.
D. Start by writing tests for the first file in the directory structure and proceed sequentially through the codebase.

Answer: C

Explanation:
For open-ended and complex tasks like adding tests to a legacy codebase, the best decomposition strategy involves first mapping the structure and identifying high-impact areas, then creating a prioritized plan that can dynamically adapt as the agent discovers dependencies during implementation.

NEW QUESTION # 104
How should an architect design crash recovery for a multi-agent exploration system at quicktechie.com?

A. Automatically trigger a /clear command to gracefully reset the environment.
B. Use structured agent state exports (manifests) that the coordinator loads on resume and injects into agent prompts.
C. Parse natural language checkpoints directly from the assistant's unstructured text responses.
D. Rely entirely on the 4. claude .json file to store conversational memory.

Answer: B

Explanation:
Designing crash recovery requires persistent, structured state. Best practice dictates that each agent exports its state to a known location as a manifest, which the coordinator can then load and inject upon resumption to seamlessly recover the session state.

NEW QUESTION # 105
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