Anthropic Claude Certified Architect - Foundations : CCAR-F

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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

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. Remove all severity-related instructions and allow the model to use its default judgment about which findings to report.
B. Instruct the model to report all findings with confidence and severity tags, deferring filtering to a downstream step.
C. Enable extended thinking and instruct the model to reason step by step about every code change before producing its review.
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 2

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.
You've documented API error handling conventions in a CLAUDE.md file at your project root, specifying that endpoint handlers should use a custom ApiError class. After several sessions, you notice Claude Code sometimes follows these conventions and sometimes uses generic try/catch blocks with string messages. The inconsistency appears random across different coding sessions. What's the most efficient first diagnostic step?

A. Create path-specific rules in .claude/rules/handlers.md with YAML frontmatter scoping the error handling instructions to your API handler files.
B. Search for conflicting instructions in ~/.claude/CLauDe.md or ~/.claude/rules/ that might override your project conventions.
C. Run /memory to check which memory files are loaded and verify your CLAUDE.md is included.
D. Add a more detailed code examples to your CLAUDE.md showing the exact ApiError usage pattern for different endpoint types.

Question 3

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 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.
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 apply stricter evidence requirements to rendering-related issues.

Question 4

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 is configuring MCP servers in Claude Code. You want to add a shared venue lookup server that all team members should have access to, and you personally want to add an experimental music playlist server that only you are testing. Which configuration approach correctly applies MCP server scopes?

A. Add both servers to your local ~/.claude.json
B. Add venue server to ~/.claude.json and playlist server to .mcp.json
C. Add venue server to .mcp.json and playlist server to ~/.claude.json.
D. Add both servers to the project-level .mcp.json file

Question 5

The web search agent has gathered several relevant sources for a research topic. The document analysis agent now needs to examine these sources. How does information typically flow between these two specialized subagents?

A. The coordinator agent receives the web search agent's output and includes relevant findings in the prompt when invoking the document analysis agent.
B. Both agents access a shared memory store where the web search agent writes findings and the document analysis agent reads them.
C. The web search agent directly invokes the document analysis agent, passing the discovered sources as parameters.
D. The agents communicate through an event-driven message queue, with the document analysis agent subscribing to web search completion events.

Solutions: