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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 2: Tool Design & MCP Integration	18%	- Tool definition and best practices 1. Tool descriptions and selection logic 2. Error handling and validation - Model Context Protocol (MCP) 1. Tools, resources, and prompts integration 2. MCP server and client setup
Topic 3: 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
Topic 4: 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 5: Prompt Engineering & Structured Output	20%	- Structured data generation 1. Output validation and reliability 2. JSON schema enforcement - Advanced prompting techniques 1. Few-shot and chain-of-thought prompting 2. System prompts and role framing

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q67-Q72):

NEW QUESTION # 67
A well-designed structured error response for an MCP tool should include which of the following standard fields? Choose 2 correct answers.

A. errorCategory
B. temperature_adjustment
C. system_prompt_override
D. isError

Answer: A,D

Explanation:
Structured error responses in MCP rely on standard fields like isError, errorCategory, isRetryable, and context to give the agent a clear path for recovery. They do not alter underlying model parameters like temperature or the system prompt.

NEW QUESTION # 68
You've configured the system so that all four subagents have access to the complete set of
18 tools. During testing, agents frequently call tools outside their specialization - the synthesis agent attempts web searches, and the report generator tries to analyze documents. What is the primary cause of this poor tool selection behavior?

A. Choosing from 18 tools instead of 4-5 relevant ones increases decision complexity beyond reliable selection thresholds.
B. The agents' role descriptions in their system prompts conflict with having access to tools outside that role.
C. The tool definitions consume too much context window space, leaving insufficient room for task content.
D. The coordinator cannot track which capabilities each subagent has, leading to misrouted tasks.

Answer: A

Explanation:
Providing all agents with access to a large set of tools increases the cognitive load for tool selection. When the number of options grows beyond a manageable threshold, agents are more likely to misuse tools outside their specialization, reducing efficiency and accuracy.

NEW QUESTION # 69
When implementing a Model Context Protocol (MCP) server, what is the primary architectural purpose of defining an MCP Resource instead of an MCP Tool?

A. To expose data catalogs or static content that can be fetched by the client and injected directly into prompts without requiring the agent to formulate a tool call.
B. To manage environment variables and authentication secrets safely across the development team.
C. To provide reusable conversation templates consisting of predefined user and assistant messages for the agent.
D. To expose executable functions that perform side-effects or mutations on external backend systems.

Answer: A

Explanation:
MCP Resources are used to expose data (like documents, schemas, or issue summaries) that clients can read and inject directly into prompts, bypassing the need for exploratory tool calls. Tools are for taking actions, and Prompts are for reusable conversation templates.

NEW QUESTION # 70
You are designing an MCP tool to look up customer orders. Which approach to tool descriptions is recommended to ensure reliable tool selection by Claude?

A. Keep the description under 10 words to save context window tokens.
B. Include the input format, example queries, edge cases, and boundaries explaining when to use it versus similar tools.
C. Leave the description blank and rely on Claude's semantic understanding of the tool's name.
D. Use the description solely to list the required JSON properties.

Answer: B

Explanation:
Tool descriptions act as the primary mechanism LLMs use for tool selection. A well-designed tool description must be detailed and include input formats, example queries, edge cases, and explicit boundary instructions explaining when to use it versus similar tools to prevent misrouting.

NEW QUESTION # 71
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When analyzing complex legal cases that cite multiple precedents, the document analysis subagent processes each sequentially. A landmark case citing 12 precedents takes over 3 minutes to analyze completely. What's the most effective way to reduce this latency while preserving the coordinator's ability to monitor and debug the system?

A. Implement a message queue where precedent analysis tasks are processed asynchronously by a pool of worker agents
B. Create a recursive agent hierarchy where analysis agents subdivide work among child agents until reaching single-precedent granularity
C. Enable the document analysis subagent to spawn its own specialized subagents dynamically when it encounters cases with many citations
D. Have the coordinator spawn parallel document analysis subagents, each handling a subset of precedents, then aggregate results before synthesis

Answer: D

Explanation:
Parallelizing independent precedent analyses reduces latency, while keeping delegation at the coordinator level preserves centralized visibility, tracing, and debugging. Recursive delegation would make monitoring more difficult.

NEW QUESTION # 72
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