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

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.
The agent verifies customer identity through a multi-step process before resetting passwords. During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened.
What's the most likely cause of this behavior?

A) The prompt lacks instructions telling Claude to remember information across multiple exchanges.
B) The conversation history isn't being passed in subsequent API requests.
C) The verification tool is clearing the agent's internal state after each successful validation step.
D) Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.

3. 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.
Your agent is handling a billing dispute. After calling get_customer and lookup_order , it identifies that the dispute involves a promotional pricing error requiring manager approval-beyond the agent's authorization level.
How should the workflow handle this mid-process escalation?

A) Attempt the refund with process_refund anyway, escalating only if the system rejects the transaction.
B) Call escalate_to_human , passing only the customer's original message.
C) Compile a structured handoff with customer details, order info, and the identified issue before calling escalate_to_human .
D) Persist the complete conversation and tool response history to a database, then call escalate_to_human with a reference ID.

4. 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.
Compliance requires that refunds exceeding $500 must automatically escalate to a human agent-this rule cannot be left to model discretion. Despite clear system prompt instructions, production logs show the agent occasionally processes high-value refunds directly (3% failure rate).
How should you achieve guaranteed compliance?

A) Strengthen the system prompt with emphatic language: "CRITICAL POLICY: Refunds over $500 MUST trigger human escalation. NEVER process these directly."
B) Modify the refund tool to return an error with message "Amount exceeds policy limit-please escalate" when the threshold is exceeded.
C) Add few-shot examples to the prompt showing correct escalation behavior at various refund amounts ($400, $500, $600).
D) Implement a hook to intercept tool calls, when the refund process amount exceeds $500, block it and invoke human escalation.

5. 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.
You need to add a date validation check ensuring event dates are in the future. This requires adding a conditional statement to one existing function in a single file.
What is the most appropriate approach?

A) Enter plan mode to analyze how the validation might impact other parts of the reservation flow.
B) Enter plan mode first to create a detailed implementation strategy before making the change.
C) Start with extended thinking mode enabled to ensure thorough reasoning about the validation logic.
D) Use direct execution to make the change.

Solutions: