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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: 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. Task spawning and tool management 2. Session state and context handling
Topic 3: Prompt Engineering & Structured Output	20%	- Structured data generation 1. JSON schema enforcement 2. Output validation and reliability - Advanced prompting techniques 1. System prompts and role framing 2. Few-shot and chain-of-thought prompting
Topic 4: 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 5: 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. MCP server and client setup 2. Tools, resources, and prompts integration

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q12-Q17):

NEW QUESTION # 12
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.
You're implementing a complex graph traversal algorithm with specific performance requirements and edge cases to handle (disconnected nodes, cycles, weighted edges). You want to structure your workflow for efficient iterative refinement with Claude. What approach will most effectively enable progressive improvement across multiple iterations?

A. Provide Claude with a detailed natural language specification of the algorithm, including all requirements and edge cases. Review each output manually and provide descriptive feedback on what behavior needs to change.
B. Write a test suite covering expected behavior, edge cases, and performance requirements before implementation. Ask Claude to write code that passes the tests, then iterate by sharing test failures with each refinement request.
C. Have Claude extensively research the algorithm and create a detailed implementation plan using extended thinking, then implement the complete solution based on that plan.
D. Provide Claude with a reference implementation from documentation, then ask it to rewrite the code to match your codebase style and add the required edge case handling, comparing outputs against the reference.

Answer: B

Explanation:
Tests provide an executable specification and objective feedback for each iteration. Claude can implement against the suite, run it, inspect failures, and progressively refine the algorithm until correctness and performance requirements are met. Anthropic recommends running tests and fixing failures as an iterative Claude Code workflow.

NEW QUESTION # 13
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 is configuring MCP servers in Claude Code. You want to add a shared venue lookup server that all team members should have access to, and you personally want to add an experimental music playlist server that only you are testing. Which configuration approach correctly applies MCP server scopes?

A. Add both servers to the project-level .mcp.json file
B. Add both servers to your local ~/.claude.json
C. Add venue server to ~/.claude.json and playlist server to .mcp.json
D. Add venue server to .mcp.json and playlist server to ~/.claude.json.

Answer: D

Explanation:
Project-scoped MCP servers are stored in .mcp.json and can be shared through version control with the whole team. Personal local or user-scoped servers are stored in ~/.claude.json and remain private to the individual user.

NEW QUESTION # 14
Your structured data extraction pipeline implements a validation-retry loop. When an extracted value fails a business logic check (e.g., line items do not sum to the total), how should the retry prompt be structured?

A. Automatically increase the model temperature and resubmit the original prompt.
B. Send a follow-up request including the original document, the failed extraction, and the specific validation error details.
C. Switch to a more capable model and restart the session without the failed extraction history.
D. Return a generic message such as There were errors in your extraction. Please try again.

Answer: B

Explanation:
A successful validation-retry loop requires appending specific error details (not generic messages) to the prompt and supplying both the source document and the failed extraction. Generic retry messages give the model no signal for what to fix.

NEW QUESTION # 15
The agent attempts to call a lookup_order tool, but the backend database is down due to a timeout. What structured error response should the tool return to the agent?

A. {"error : " A fatal exception that immediately terminates the agentic loop.
B. A generic string " Operation failed"} to avoid leaking infrastructure details to the LLM.
C. An empty JSON array [J so the agent can smoothly transition to an alternative tool.
D. A structured object containing isError: true, an error category of 'timeout', isRetryab1e: true, and context of what was attempted.

Answer: D

Explanation:
Silently returning an empty array for an access failure is a catastrophic anti-pattern that makes the agent think the data doesn't exist. Returning structured error metadata (isError, errorCategory, isRetryable, context) allows the agent to make intelligent recovery or escalation decisions.

NEW QUESTION # 16
A chatbot frequently receives greetings, policy questions, and technical support requests. Which architecture improves maintainability?

A. Disable retrieval.
B. Prompt routing based on request type.
C. One prompt for every scenario.
D. Maximum temperature.

Answer: B

Explanation:
Prompt routing directs requests to specialized workflows optimized for different tasks. Separate prompts and tools improve accuracy, simplify maintenance, and reduce conflicts between unrelated instructions.

NEW QUESTION # 17
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