The 6 CCAR-F Exam Scenarios Explained

The CCAR-F exam shows you four of six scenarios, picked at random. Each scenario frames 10–15 questions, so knowing the setup in advance saves you minutes of reading and helps you spot what each question is really testing.

🎬 Scenario 1: Customer Support Resolution Agent

An Agent SDK agent handles returns, billing disputes and account issues through MCP tools such as get_customer, lookup_order, process_refund and escalate_to_human, targeting 80%+ first-contact resolution.

What it tests: prerequisite gates (verify before refund), interception hooks for thresholds, multi-concern requests, structured errors, escalation calibration, handoff summaries and preserving case facts in long conversations.

🎬 Scenario 2: Code Generation with Claude Code

A team uses Claude Code for generation, refactoring, debugging and documentation with custom slash commands and CLAUDE.md.

What it tests: the CLAUDE.md hierarchy, .claude/rules/ with path globs, commands vs skills, skill frontmatter, plan mode vs direct execution, iterative refinement and context degradation.

🎬 Scenario 3: Multi-Agent Research System

A coordinator delegates to search, document-analysis, synthesis and report subagents to produce cited reports.

What it tests: decomposition breadth, explicit context passing, parallel Task calls, scoped tools, error propagation, conflicting sources and provenance.

🎬 Scenario 4: Developer Productivity with Claude

An Agent SDK assistant explores codebases and automates chores using built-in tools (Read, Write, Edit, Bash, Grep, Glob) and MCP servers.

What it tests: built-in tool selection, incremental exploration, MCP scoping and resources, tool descriptions that beat built-ins, session resume and fork.

🎬 Scenario 5: Claude Code for Continuous Integration

Claude Code reviews pull requests and generates tests in CI, with a focus on actionable, low-false-positive feedback.

What it tests: -p, --output-format json + --json-schema, CLAUDE.md as CI context, independent reviewers, explicit criteria, multi-pass review and batch-vs-synchronous decisions.

🎬 Scenario 6: Structured Data Extraction

Unstructured documents are turned into JSON validated against schemas and passed downstream.

What it tests: tool_use with JSON schemas, nullable fields, enum design, validation-retry loops, batch processing, confidence calibration and human-review routing.

🧪 Exam-Style Practice Questions

Try these before opening the answers. They use the same scenario style as the real exam.

Question 1. A customer message says: "My order arrived damaged, I was double-charged, and I want to change my email." What's the best agent strategy?

• A. Answer the easiest issue first and end the session
• B. Handle only the first issue and ask them to open new tickets
• C. Split into three items, investigate each (in parallel where possible) using shared context, then give one unified reply
• D. Escalate, because it's multi-issue

Question 2. Your CI posts Claude's review findings as inline PR comments. What output configuration should you use?

• A. Markdown output
• B. Default text output parsed with regexes
• C. `–verbose`
• D. `–output-format json` with `–json-schema` defining the findings structure

Question 3. The synthesis output should show readers which sections rest on thin evidence because a source failed. What should you add?

• A. Nothing; keep it clean
• B. Remove the affected sections
• C. Coverage annotations marking well-supported findings versus gaps from unavailable sources
• D. A generic disclaimer

📝 Assignment

Do these tasks in order. Each one has a Done when check, so you know when it is complete. The stretch task is optional but close to what the exam expects from an architect. Keep your answers in one notes file or Git repository, and revisit them in your final week of revision together with the cheat sheet in the last lesson of this course.

1. Task 1: For each scenario, write one sentence describing a system you have built or seen that is similar.
   ✅ Done when: Each of the six scenarios has one sentence describing a real or familiar system, so you can picture it on exam day.
2. Task 2: Map each scenario to its two most-tested domains without looking back at the lesson.
   ✅ Done when: Your mapping matches the lesson for at least five of the six scenarios when you compare it afterwards.
3. Stretch: draft one exam-style question of your own for Scenario 6 with four options and an explanation.
   ✅ Done when: Your question has one clearly correct answer, three plausible distractors, and an explanation of why each distractor is wrong.

❓ Frequently Asked Questions

🤔 How many scenarios appear on the CCAR-F exam?

Four, picked at random from a bank of six. Each frames roughly 15 questions.

🤔 Which scenario is best for QA engineers?

Scenario 5, Claude Code for Continuous Integration, and Scenario 6, Structured Data Extraction, are closest to test-automation work.

🤔 Do I need to memorise the tool names in the scenarios?

No, but knowing the setup (for example get_customer before process_refund) saves reading time and helps you spot what a question tests.

🤔 Do the mock tests use the same scenarios?

Yes. The five mock tests on this site rotate through all six scenarios so you meet each one at least twice.

🏁 Summary

• Scenarios 1, 3 and 6 lean on Domains 1, 4 and 5; Scenarios 2, 4 and 5 lean on Domains 2 and 3.
• Read the scenario once, carefully — every question in that block assumes you remember it.
• Our five mock tests rotate through all six scenarios so you meet each at least twice.

Next, continue with Claude Agentic Loop: stop_reason, Tool Results and Anti-Patterns.

Independent study material by SQA Tools. Not affiliated with, endorsed by or sponsored by Anthropic. Claude is a trademark of Anthropic. Mock questions are original practice items, not real exam content. Exam facts checked against Anthropic's official exam guides and certification FAQ on 11 November 2026.