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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

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 must extract event details from calendar invitations and output JSON that strictly conforms to a schema with fields for title, date, time, location, and attendees. Downstream systems reject any malformed or non-conformant JSON.
What approach provides the most reliable schema compliance?

A. Include detailed JSON formatting instructions and the target schema in your prompt, then parse Claude's text response as JSON.
B. Define a tool with your target schema as input parameters and have Claude call it with the extracted data.
C. Append instructions like "Output only valid JSON matching the schema exactly" and implement retry logic to re-prompt when JSON parsing fails.
D. Pre-fill Claude's response with an opening brace to force JSON output, then complete and parse the response.

Question 2

Your automated code review is missing genuine bugs in pull requests. Investigation reveals that the review prompt includes this instruction: "Only flag critical issues that would definitely cause production failures. Ignore minor concerns and anything you are uncertain about." Developers confirm that some missed findings are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with every finding tagged with metadata, and actionable. Which prompt change both removes the cause of the suppressed findings and preserves structured, tagged output for downstream filtering?

A. Instruct the model to report all findings with confidence and severity tags, deferring filtering to a downstream step.
B. Enable extended thinking and instruct the model to reason step by step about every code change before producing its review.
C. Remove all severity-related instructions and allow the model to use its default judgment about which findings to report.
D. Add a second review pass that rereads the diff using the same prompt and looks for anything the first pass may have missed.

Question 3

A customer writes: "I've been going back and forth on this return for days. I just want to speak to someone who can actually help me." The agent has confirmed via lookup_order that the return is straightforward - within policy and eligible for immediate processing. What should the agent do?

A. Acknowledge frustration, inform them this is resolvable now, and offer to complete it or escalate
B. Call escalate_to_human immediately to honor the customer's request
C. Ask what specifically hasn't worked in previous attempts before deciding whether to escalate or resolve automatically
D. Process the refund via process_refund to resolve the underlying issue, then inform them it's complete

Question 4

You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer used Claude Code yesterday to investigate authentication flows in a legacy monolith, building up significant context over a 2-hour session. Today she wants to continue that specific investigation. She's worked on three other codebases since then and knows the session was named "auth-deep-dive".
How should she resume?

A. Use --continue to pick up where the most recent conversation left off
B. Use --session-id with the UUID from yesterday's session transcript file
C. Start fresh and re-read the same files
D. Use --resume auth-deep-dive to load that specific session by name

Question 5

Your agent uses three tools: get_property_details(property_id) returns data including street address, get_price_history(property_id) returns historical pricing, and get_neighborhood_info(address) returns area statistics. You observe that get_neighborhood_info always requires get_property_details first just to extract the address, even when users specify the property by ID. This creates unnecessary latency and failure coupling - if the first call fails, the neighborhood request also fails. What tool design change best addresses this?

A. Add retry logic and timeout handling to get_property_details.
B. Create a lookup_address(property_id) helper tool for retrieving addresses.
C. Change get_neighborhood_info to accept property_id, resolving the address internally.
D. Consolidate into a single get_property_with_neighborhood(property_id) tool returning both datasets.

Solutions: