Anthropic CCA-F Review Guide | CCA-F Latest Test Materials

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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Agentic Architecture & Orchestration	27%	- Agent design patterns 1. Hub-and-spoke multi-agent systems 2. Agent loops and control flow - Anthropic Agent SDK usage 1. Session state and context handling 2. Task spawning and tool management
Claude Code Configuration & Workflows	20%	- Configuration files and structure 1. Custom commands and workflows 2. CLAUDE.md and rules system - CI/CD and development integration 1. Pipeline automation and review 2. Plan mode vs direct execution
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
Prompt Engineering & Structured Output	20%	- Advanced prompting techniques 1. Few-shot and chain-of-thought prompting 2. System prompts and role framing - Structured data generation 1. JSON schema enforcement 2. Output validation and reliability
Context Management & Responsible AI	15%	- Safety and compliance 1. Refusal handling and risk mitigation 2. Constitutional AI principles - Context window optimization 1. Token management and truncation strategies 2. Context retention and summarization

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q104-Q109):

NEW QUESTION # 104
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth - having the agent propose specific code changes for each - before deciding which to implement. What's the most effective way to structure this exploration?

A. Resume yesterday's session and explore both approaches sequentially within the same conversation thread.
B. Start two fresh sessions, manually providing a summary of yesterday's analysis findings to establish context.
C. Resume yesterday's session to explore the first approach, then start a new session for the second, manually recreating the original context.
D. Use fork_session to create two branches from yesterday's analysis, exploring one approach in each fork.

Answer: D

Explanation:
Forking preserves the accumulated authentication-module context while giving each refactoring approach an independent session, preventing one exploration from influencing the other.
Anthropic's Agent SDK supports creating a new session ID when resuming with session forking enabled.

NEW QUESTION # 105
Where should a development team configure a shared Jira MCP server to ensure credentials are NOT leaked in version control?

A. In the project-level .mcp.json file, using environment variable expansion syntax like
B. Inside a dedicated secrets. json file that is committed to the repository.
C. As inline comments within the CLAUDE. md file.
D. In the user-level 4. claude. json file, requiring each team member to manually script the configuration.

Answer: A

Explanation:
Shared team MCP configurations belong in the project-level .mcp.json file. To prevent secret leaks in version control, you must use environment variable expansion (e.g., ${JIRA_TOKEN}) so the secret stays in the local environment and not in the code.

NEW QUESTION # 106
An enterprise wants Claude responses to remain professional across every interaction. Where should tone instructions primarily reside?

A. System prompt
B. Retrieved documents
C. API key description
D. User prompt only

Answer: A

Explanation:
Persistent behavioral guidance belongs in the system prompt. Defining tone centrally ensures consistent communication across conversations regardless of varying user inputs or retrieved knowledge.

NEW QUESTION # 107
During a long-running customer support session, critical details like the customer's account number and order amount are lost due to progressive summarization. What architectural pattern prevents this context degradation?

A. Increase the frequency of the summarization algorithm to trigger every 3 turns.
B. Restart the session entirely and require the user to input their context again.
C. Write all customer details to an external database and run a lexical search on every turn.
D. Store critical transactional details in an immutable 'case facts' block positioned at the beginning of the context window.

Answer: D

Explanation:
Progressive summarization silently destroys specific details. The best mitigation is extracting and persisting key structured data into an immutable 'case facts' block at a high-recall position (the start of the context window) that is never summarized.

NEW QUESTION # 108
Why is configuring the exact same Claude session to review its newly generated pull request code considered an architectural anti- pattern?

A. The Message Batches API does not support multi-turn tool calling within a single request.
B. Tools invoked during the first pass are permanently locked and cannot be reused in the review pass.
C. It exceeds the maximum token limit for the context window during the generation phase.
D. The reviewer retains the reasoning context and assumptions from the generation phase, creating confirmation bias.

Answer: D

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 # 109
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