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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

1. You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
During testing, you find that when a customer says "I need a refund for my recent purchase," the agent calls process_refund immediately-but populates the required order_id parameter with a plausible-looking but fabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call fails because the fabricated ID doesn't exist.
Which change directly addresses the root cause of the agent fabricating the order_id value?

A) Add server-side validation that checks whether the order_id exists in your database before executing the refund, returning an error to the agent if not found.
B) Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.
C) Switch tool_choice from " auto " to " any " to force the agent to make a tool call on every turn.
D) Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.

2. You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
A customer raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?

A) Implement sliding window context that retains the most recent 30 turns.
B) Rely on MCP tools to re-fetch relevant information on demand when the customer references earlier issues.
C) Summarize earlier turns into a narrative description, preserving full message history only for the active issue.
D) Extract and persist structured issue data (order IDs, amounts, statuses) into a separate context layer.

3. 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 wants Claude to follow a detailed code review checklist (8 items covering API changes, test coverage, documentation, security, etc.) when reviewing pull requests. The team also uses Claude extensively for other tasks: writing new features, debugging production issues, and generating documentation. Currently, developers paste the checklist at the start of each review session.
Which approach best addresses this workflow need?

A) Configure plan mode as the default for code review sessions.
B) Create a dedicated review subagent with the checklist embedded in its configuration.
C) Add the checklist to the project's CLAUDE.md file under a "Code Review" section.
D) Create a /review slash command containing the checklist, invoked when starting reviews.

4. You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your system has been running for 3 weeks and human reviewers have corrected 847 extractions. Analysis reveals a recurring pattern: when recipes use informal measurements like "a handful" or "a splash," the model either invents specific amounts or leaves fields empty-accounting for 23% of all corrections.
How should you use this feedback to improve extraction accuracy?

A) Fine-tune the model on the 847 corrected extractions.
B) Update your JSON schema to add a "measurement_type" enum field (precise/informal).
C) Add few-shot examples to your prompt demonstrating correct handling of informal measurements- extracting them verbatim rather than converting or omitting them.
D) Implement a post-processing layer that uses pattern matching to detect informal measurement phrases in source text and automatically populate values when the extraction is empty.

5. When implementing your lookup_order MCP tool, the backend sometimes returns errors-for example,
"Order not found" or temporary database failures. What is the correct pattern for communicating these errors back to the agent?

A) Log the error server-side and return an empty result to avoid confusing the model.
B) Return a successful response with a status field indicating the error type.
C) Throw an exception from the tool handler so the agent framework can catch and log it.
D) Return the error message in the tool-result content with the isError flag set to true.

Solutions: