Anthropic CCAR-F : Claude Certified Architect - Foundations

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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 JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your invoice extraction uses tool use with strict JSON schemas. JSON syntax errors never occur, but 12% of extractions fail semantic validation--for example, line-item amounts do not sum to the extracted total, or vendor IDs do not match valid formats. These failures currently route to manual review.
What is the most effective approach to reduce manual-review volume while maintaining accuracy?

A. Implement post-processing logic that automatically corrects common errors, such as recalculating totals from line items when sums do not match.
B. Retry the extraction up to three times when validation fails, accepting the first result that passes validation.
C. Add stricter schema constraints with detailed field descriptions to prevent the model from initially generating invalid values.
D. When validation fails, make a follow-up request containing the document, extraction, and validation errors so the model can correct the result.

Question 2

A legal department requires Claude to answer only when sufficient evidence exists in retrieved documents. What should Claude do if evidence is insufficient?

A. Search social media automatically.
B. Increase creativity.
C. Invent a likely answer.
D. Clearly indicate insufficient information.

Question 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's CLAUDE.md includes a rule: "Use 4-space indentation and always run Prettier formatting." Despite this, code reviews reveal that roughly 30% of files Claude Code generates use inconsistent formatting - sometimes 2-space indentation, sometimes missing trailing commas. Adding emphasis ("IMPORTANT: You MUST use Prettier formatting") reduces violations to about 15%, but doesn't eliminate them. What is the most effective way to ensure all generated code is consistently formatted?

A. Add a Stop hook with a prompt-based check that evaluates whether generated code follows formatting standards and prompts Claude to fix violations.
B. Extract the formatting rules into a dedicated skill that Claude loads automatically when generating code, with more detailed examples of correct formatting.
C. Configure a PostToolUse hook with an Edit|Write matcher that automatically runs Prettier on each file Claude modifies.
D. Split the formatting rules into path-scoped .claude/rules/files that load when Claude works on matching file types.

Question 4

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.
Your automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at
82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to
68%.
How should you address this trade-off to improve detection across both categories?

A. Split the review into separate focused prompts--one for security and API design and another for business logic--each with dedicated examples, and then consolidate the findings before posting.
B. Replace the few-shot examples with a detailed checklist of specific logic edge cases to verify, such as division by zero in score calculations and boundary conditions in grading thresholds.
C. Provide the full repository as context instead of only the changed files and surrounding code, giving the model deeper visibility into business-logic patterns.
D. Upgrade to a more capable model tier because its stronger reasoning will handle both concern types in one prompt and eliminate the recall trade-off.

Question 5

Your product search tool queries an external catalog API and returns matching items. In production, you observe the agent frequently retries searches immediately after receiving zero results, treating "no matches found" as a failure requiring retry. The external API returns HTTP 200 with an empty results array - a valid response. How should you restructure the tool's result to help the agent correctly interpret empty result sets?

A. Return a result object with isError: true and a message explaining no products matched.
B. Return a structured result with a success boolean and results array, reserving isError: true for actual execution failures only.
C. Return a natural language string describing the outcome, allowing the agent to interpret the result contextually based on the message content.
D. Add a suggestions field containing alternative search strategies when results are empty, helping guide the agent toward more productive follow-up queries.

Solutions: