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

Your update_user_profile tool accepts a user_id (required) and an optional fields_to_update object. In testing, Claude frequently omits user_id or passes incorrectly structured data. What is most critical for helping Claude understand what parameter values to provide?

A. Strict JSON Schema type constraints marking user_id as required and defining fields_to_update as an object type
B. Verbose parameter names encoding format hints, such as user_id_string_uuid_format
C. Detailed error responses explaining why invalid parameter values were rejected
D. Clear parameter descriptions explaining expected format, such as "user_id: UUID of the user to update (required)"

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

Question 3

Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true, "content": [{"type": "text", "text": "Operation failed"}]}. The agent cannot distinguish between error types. What's the most effective improvement?

A. Enhance error responses with structured metadata: include errorCategory (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.
B. Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.
C. Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.
D. Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.

Question 4

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.
After deployment, you find that 12% of extractions contain semantic errors that pass JSON Schema validation--for example, a duration such as "30 minutes" is incorrectly placed in an ingredient-quantity field. Human reviewers have the capacity to check only 20% of extractions.
Which approach most effectively allocates reviewer attention?

A. Have the model output field-level confidence scores, and then calibrate review thresholds using a labeled validation set.
B. Randomly sample 20% of extractions for review, using corrections to track accuracy and identify error patterns.
C. Prioritize the review of all extractions where required fields are empty or explicitly marked as not found.
D. Review all extractions from documents with formatting anomalies, such as unusual layouts or mixed content types.

Question 5

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 CI linter, (2) findings on automatically generated template code under src/gen/*, and (3) rendering-helper patterns that are intentional project conventions but are 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 real 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. Configure separate GitHub Actions workflow files for each code area: one for generated code with findings suppressed, one for rendering code with custom instructions, and one general workflow for everything else.
C. 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 impose stricter evidence requirements on rendering-related issues.
D. Add detailed explanations to the project's CLAUDE.md describing intentional patterns, stating that CI handles linting, and identifying src/gen/ as automatically generated code.

Solutions: