Pass with professional CCAR-F actual quiz materials

Anthropic CCAR-F Exam Overview:

Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
Production monitoring shows that the research phase takes longer than expected. Analysis reveals that the coordinator invokes the web-search subagent, waits for its response, and then invokes the document-analysis subagent. These tasks are independent; neither requires the other's output.
How should you modify the system to run these subagents concurrently?

A. Add instructions explaining the performance benefits of parallel execution and requesting that the coordinator invoke both subagents simultaneously.
B. Structure the coordinator to emit both Agent tool calls--formerly Task tool calls--in a single response rather than across separate conversation turns.
C. Switch both subagents to a Haiku-tier model instead of Sonnet to reduce their individual execution times.
D. Create an asynchronous orchestration layer that starts separate coordinator-subagent pairs in parallel and aggregates their results.

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.
Your team has three requirements for Claude Code's behavior in your project
1. Claude must never modify files in the db/migrations/ directory
2. Claude should prefer your custom logging module over console.log
3. All TypeScript files must be auto-formatted with Prettier after
every edit
All three are currently written as instructions in your project's CLAUDE.md. During a complex refactoring session, a developer discovers that Claude edited a migration file, violating requirement #1. How should you restructure these requirements across Claude Code's configuration mechanisms?

A. Configure hooks for all three: a PreToolUse hook script that blocks Edit calls targeting db/migrations/,a PreToolUse hook script that adds logging convention context before edits, and a PostToolUse hook that runs Prettier after TypeScript edits.
B. Move all three requirements into .claude/rules/ as path-scoped rules: one targeting db/migrations/** that forbids editing those files, and others targeting **/*.ts for the logging convention and formatting instruction.
C. Rewrite all three requirements in CLAUDE.md using stronger directive language and add few- shot examples that demonstrate Claude refusing to edit migration files and running Prettier after edits.
D. Add Edit(./db/migrations/**) to permissions.deny in the project settings, keep the logging preference in CLAUDE.md, and add a PostToolUse hook on the Edit tool that runs Prettier on changed TypeScript files.

Question 3

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 has been operating with 100% human review for 3 months. Analysis shows that extractions with model confidence 90% have 97% accuracy overall. To reduce reviewer workload, you plan to automate high-confidence extractions.
Before deploying, what validation step is most critical?

A. Run a two-week pilot routing 25% of high-confidence extractions directly to downstream systems and monitor error reports.
B. Compare accuracy at different confidence thresholds (85%, 90%, 95%) to find the optimal cutoff that maximizes automation while minimizing errors.
C. Analyze accuracy by document type and field to verify high-confidence extractions perform consistently across all segments, not just in aggregate.
D. Verify that 97% accuracy meets requirements for all downstream systems that consume the extracted data.

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 review calls the Claude API for each pull request, using tool_use with a report_findings tool that returns a JSON array of finding objects. Each object contains file_path, line_number, severity, category, and description. During testing on a large pull request touching more than 30 files, the response reaches the max_tokens limit and is truncated in the middle of the JSON, causing your pipeline's parser to fail.
What is the most effective way to handle this?

A. Split the review into multiple API calls that each analyze a subset of the changed files, and then merge the resulting findings arrays.
B. Switch from tool_use to prompting Claude to return findings as a Markdown list.
C. Increase max_tokens to the model's maximum and instruct Claude to keep each finding description under 50 words.
D. Add retry logic that detects truncated JSON and resends the request with instructions to report only critical and high-severity findings.

Question 5

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. Remove all severity-related instructions and allow the model to use its default judgment about which findings to report.
D. Instruct the model to report all findings with confidence and severity tags, deferring filtering to a downstream step.

Solutions: