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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.
You're implementing the escalation logic for when the agent should call escalate_to_human . Your team proposes four different approaches for triggering escalation.
Which approach will most reliably identify cases that genuinely require human intervention?

A) Configure the agent to escalate after three consecutive tool calls that fail to resolve the customer's stated issue, ensuring a reasonable attempt before involving a human.
B) Implement sentiment analysis that monitors for frustration indicators (negative language, repeated questions, exclamation marks) and triggers escalation when the frustration score exceeds a configured threshold.
C) Instruct the agent to escalate when the customer requests a human, when the issue requires policy exceptions, or when the agent cannot make meaningful progress.
D) Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.

2. 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 extraction pipeline processes contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30-day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies.
What's the most effective approach to improve extraction accuracy for documents with amendments?

A) Preprocess documents with a classifier that identifies and removes superseded sections before the main extraction step.
B) Implement post-extraction validation using pattern matching to detect amendments and flag those extractions for manual review.
C) Add prompt instructions to always extract the most recent amendment value and ignore superseded original terms.
D) Redesign the schema so amended fields capture multiple values, each with source location and effective date.

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.
Anthropic's tool use documentation states: "Write instructive error messages. Instead of generic errors like
'failed', include what went wrong and what Claude should try next." A billing dispute agent uses lookup_order , which catches all exceptions and returns a tool_result with is_error: true and the message
"Tool execution failed". Monitoring shows two failure modes: the agent retries the identical call until hitting the turn limit, or it immediately calls escalate_to_human without trying alternative tools.
Which change follows the documented recommendation and gives Claude the information it needs to select the correct recovery action for each error type?

A) Remove is_error: true and return the error details as normal tool content, so Claude reasons about the response as data rather than treating it as a flagged failure condition that biases retry behavior.
B) Return error-type-specific messages with is_error: true , e.g., "Order not found-try get_customer to search by phone" for data errors and "Database timeout (transient)-retry should succeed" for infrastructure errors.
C) Add an error classification step in the agentic loop that intercepts tool errors before Claude sees them, then routes to hardcoded retry or escalation logic.
D) Implement retry logic with exponential backoff inside each tool implementation so transient errors are resolved transparently within the tool before any failure result is surfaced to Claude in the agentic loop.

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.
Your process_refund tool returns two types of errors: technical errors ("503 Service Unavailable",
"Connection timeout") that are transient (~5% of calls), and business errors ("Order exceeds 30-day return window", "Item already refunded") that are permanent (~12% of calls). Monitoring shows the agent wastes 3-
4 turns retrying business errors that can never succeed. Currently, both error types return only a plain text message to Claude.
What's the most effective way to reduce wasted retries while improving customer-facing response quality?

A) Return structured error responses with "retriable": false for business errors and a customer-friendly explanation for Claude to use.
B) Implement automatic retry logic at the tool layer for technical errors only, passing business errors to Claude without retries.
C) Add a check_refund_eligibility tool that must be called before process_refund to prevent business rule violations.
D) Add few-shot examples showing how to distinguish retriable from non-retriable errors by parsing error message text.

5. 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.

Solutions: