Anthropic CCAR-F

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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

1. You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
In addition to your CI pipeline, your organization has enabled Claude's managed Code Review through the Claude GitHub App on this repository, and reviews run automatically on every pull request. Reviews average 18 findings per pull request. Developer feedback reveals three categories of unwanted noise: (1) style and formatting issues already enforced by your linter in CI, (2) findings on automatically generated template code under src/gen/, and (3) rendering- helper patterns that are intentional project conventions but get flagged because they resemble common anti-patterns. Only approximately four findings per pull request are genuine logic bugs.
What is the most effective way to reduce this noise while preserving the detection of genuine issues?

A) Create a REVIEW.md file at the repository root containing skip rules for CI-enforced checks and generated files, together with a verification requirement that rendering-related findings cite a specific line demonstrating incorrect behavior.
B) Add custom review instructions to a GitHub Actions workflow file, using the action's prompt parameter to suppress duplicate lint findings, ignore generated template code, and apply stricter evidence requirements to rendering-related issues.
C) Add detailed explanations to the project's CLAUDE.md describing which patterns are intentional, that linting is handled separately by CI, and that the src/gen/ directory contains automatically generated template code.

2. You are building a structured data-extraction system using Claude. The system extracts information from unstructured documents, validates output against JSON schemas, and integrates the results with downstream systems.
Monitoring reveals that specifications sometimes appear inconsistently within source documents.
For example, a summary section might state "Battery: 4000 mAh," while the detailed specifications table states "Battery: 4200 mAh." Your current schema contains a single battery_capacity field.
This inconsistency occurs in approximately 15% of documents, and historical analysis confirms that the detailed specifications table is accurate 90% of the time.
What is the most effective approach?

A) Change the field to an array that captures every discovered value and its source location, leaving downstream systems to apply precedence rules.
B) Reject every extraction containing conflicting values and require the source document to be corrected before processing continues.
C) Add extraction instructions specifying that values from the detailed specifications table take precedence when conflicting values exist, while retaining the single-value schema.
D) Add a conflict_detected Boolean field and route every affected document for manual review.

3. Your post_content tool requires user confirmation before publishing. The current workflow displays "Ready to post to social media. Confirm?" and analytics show users approve 98% of requests within 2 seconds. Post-mortems reveal incidents where posts went to wrong accounts, were scheduled for wrong times, or contained errors - all confirmed by users without catching the mistakes. How should you redesign the confirmation workflow?

A) Auto-approve routine posts and only require explicit confirmation for unusual patterns like posting to new accounts or large audiences
B) Add a mandatory waiting period before the confirm option becomes available
C) Require users to type a confirmation phrase instead of clicking a button
D) Include the complete post text, target account, scheduled time, and platform in the confirmation request

4. During a billing dispute resolution, your agent successfully retrieves customer info via get_customer and order details via lookup_order, but when attempting to call process_refund, the tool returns a timeout error. The agent has enough information to explain the charges and verify refund eligibility, but cannot actually process the refund due to the backend failure. What approach best balances first-contact resolution with appropriate error handling?

A) Confirm the refund will be processed and close the conversation, since the system has all necessary information to complete it automatically
B) Explain the billing confirm refund eligibility, acknowledge the system issue preventing immediate processing, and offer escalation or retry later
C) Escalate immediately to a human agent since the refund action cannot be completed
D) Implement automatic retries with exponential backoff for process_refund, keeping the conversation open until the refund is successfully processed

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

Solutions: