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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
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. MCP server and client setup 2. Tools, resources, and prompts integration
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. Task spawning and tool management 2. Session state and context handling
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. Plan mode vs direct execution 2. Pipeline automation and review
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
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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q64-Q69):

NEW QUESTION # 64
Using the @anthropic-ai/claude-agent-sdk, you want to programmatically narrow the set of tools available to a specific query loop to prevent the agent from making unintended modifications. Which configuration is used to restrict tools in the SDK?

A. Set tool choice: "none" to force the model to only use search tools.
B. Inject a permissions: read-only tag into the top-level AgentDefinition.
C. Pass the allowedTooIs: [ " Read , '" ' Glob"] array inside the options object of the query() function.
D. Edit the . mcp. j son file to globally disable the write tools for the entire project.

Answer: C

Explanation:
In the @anthropic-ai/claude-agent-sdk, you restrict the toolset for a specific execution loop by passing the 'allowedTools' array (e.g., ["Read", "Glob")) in the options object of the 'query()' function. This maps to the CLI's -allowedTools flag.

NEW QUESTION # 65
When implementing the control flow for an agentic loop, which of the following approaches represent tested anti-patterns that must be avoided? Choose 2 correct answers.

A. Inspecting the stop _ reason field of the API response to govern execution continuity.
B. Parsing the assistant's natural language text output to determine loop termination.
C. Setting arbitrary iteration caps as the primary stopping mechanism.
D. Adding tool results to the conversation context between iterations so the model can incorporate new information.

Answer: B,C

Explanation:
Parsing natural language signals to determine loop termination, checking for assistant text content as a completion indicator, and setting arbitrary iteration caps as the primary stopping mechanism are explicit anti-patterns. Checking stop_reason and appending tool results are the correct approaches.

NEW QUESTION # 66
When integrating Claude Code into continuous integration and delivery pipelines, which of the following practices are highly recommended for reliability and automation? Choose 2 correct answers.

A. Using the Message Batches API to execute blocking pre-merge pull request checks to save costs.
B. Having the same session that generated the code perform the final review to ensure context consistency.
C. Executing Claude Code using the -p flag to ensure the process runs non-interactively.
D. Isolating the code generator session from the code reviewer session to eliminate confirmation bias.

Answer: C,D

Explanation:
In CI/CD pipelines, using the -p (or --print) flag is essential to prevent interactive hangs. Furthermore, isolating generator and reviewer sessions guarantees an unbiased review. Same-session reviews create confirmation bias, and the Batch API is too slow for blocking pre- merge checks.

NEW QUESTION # 67
How should field-level confidence scores from a model be utilized to optimize a human-in-the-loop workflow?

A. By setting tool_choice: 'any" whenever confidence drops below 0.5.
B. By calibrating the scores using labeled validation sets to effectively route review attention to low-confidence extractions.
C. By using them to automatically increase the max_tokens limit on subsequent runs.
D. By trusting the model's self-reported conversational confidence score to bypass human review.

Answer: B

Explanation:
To optimize human review, models should output structured field-level confidence scores that are rigorously calibrated against labeled validation sets. This ensures review thresholds are accurate, allowing high-risk or low-confidence findings to be correctly routed to human

NEW QUESTION # 68
An MCP server offers a templated resource to fetch specific documentation pages by ID. What is the correct URI syntax pattern utilized by the MCP Python SDK to represent this parameterized resource?

A. Option D
B. Option C
C. Option B
D. Option A

Answer: D

Explanation:
Templated resources in MCP utilize path parameters enclosed in braces, such as docs://documents/{doc_id}. The MCP Python SDK automatically parses these parameters from the URI and passes them as keyword arguments to the backing function.

NEW QUESTION # 69
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