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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Session state and context handling 2. Task spawning and tool management
Topic 2: Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. Tools, resources, and prompts integration 2. MCP server and client setup - Tool definition and best practices 1. Error handling and validation 2. Tool descriptions and selection logic
Topic 3: Context Management & Responsible AI	15%	- Context window optimization 1. Context retention and summarization 2. Token management and truncation strategies - Safety and compliance 1. Constitutional AI principles 2. Refusal handling and risk mitigation
Topic 4: 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
Topic 5: 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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q58-Q63):

NEW QUESTION # 58
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. Use the description solely to list the required JSON properties.
B. Include the input format, example queries, edge cases, and boundaries explaining when to use it versus similar tools.
C. Keep the description under 10 words to save context window tokens.
D. Leave the description blank and rely on Claude's semantic understanding of the tool's name.

Answer: B

NEW QUESTION # 59
Which of the following are valid applications of few-shot prompting in a structured data extraction workflow? Choose 2 correct answers.

A. Enforcing JSON syntax compliance and eliminating trailing comma errors in outputs.
B. Replacing the need for a validation-retry loop by guaranteeing 100% semantic correctness.
C. Demonstrating ambiguous-case handling, such as extracting informal measurements.
D. Demonstrating the specific desired output format, including location, issue, and severity, to achieve consistency.

Answer: C,D

Explanation:
Few-shot examples are ideal for demonstrating ambiguous-case handling and showing the specific desired output format to achieve consistency. They do not guarantee 100% semantic correctness or eliminate syntax errors as reliably as tool_use with JSON schemas.

NEW QUESTION # 60
After executing a requested tool in an agentic loop, how must the application handle the follow-up request to ensure the model can accurately reason about the next action?

A. Use the /compact command to summarize the tool output before returning it to the loop.
B. Pass the tool result inside an assistant message equipped with an is_error flag.
C. Append a user message containing a tool_result block with the matching tool_use_ld, and include the full prior conversation history.
D. Pass only the new tool result to save token space in the context window.

Answer: C

Explanation:
Because Claude is stateless, the application must provide the entire conversation history. When returning a tool result, it must be appended as a user message containing a tool_result block that explicitly includes the specific tool_use_id matching the original request.

NEW QUESTION # 61
Your customer support agent needs a strict limit preventing it from processing refunds exceeding $500. What is the most deterministic architectural method to enforce this?

A. Set the tool_choice parameter to 'auto' so the model can dynamically reject the action.
B. Provide several few-shot examples in the prompt showing the agent successfully refusing high-value refunds.
C. Implement a PostToolUse hook to intercept the process_refund tool call and block it programmatically if the amount exceeds 500.
D. Add a capitalized warning in the system prompt: 'NEVER process a refund exceeding $500.'

Answer: C

Explanation:
Critical business rules requiring deterministic enforcement must use programmatic hooks (like PreToolUse or PostToolUse). Relying on prompt instructions or few-shot examples is probabilistic and leaves a non-zero chance that the LLM will ignore the rule.

NEW QUESTION # 62
Your send_notification tool calls third-party messaging APIs. When these services time out during delivery, you cannot determine whether the message was actually sent. Currently, the tool returns is_error: true with a generic "Notification failed" message for all timeouts. Production monitoring reveals agents automatically retry these failures, frequently causing users to receive duplicate notifications. How should you modify the error response?

A. Return is_error: true with the original message content echoed back.
B. Return is_error: true with a message encouraging retry: "Delivery service temporarily unavailable.
Please retry the notification."
C. Return is_error: true with a structured field retry_safe: true for timeouts, distinguishing them from permanent failures that should not be retried.
D. Return is_error: true with a message communicating uncertainty: "Timeout - status unknown.
Message may have been sent. Avoid retry."

Answer: C

Explanation:
Adding a structured retry_safe flag distinguishes uncertain, potentially idempotent errors from permanent failures. This allows the agent to make informed decisions about automatic retries, preventing duplicate notifications while maintaining reliability for recoverable errors.

NEW QUESTION # 63
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