Anthropic CCAR-F Q&A - in .pdf

  • Exam Code: CCAR-F
  • Exam Name: Claude Certified Architect – Foundations
  • Updated: Jul 24, 2026
  • Q & A: 62 Questions and Answers
  • PDF Price: $59.98
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Anthropic CCAR-F Q&A - Testing Engine

  • Exam Code: CCAR-F
  • Exam Name: Claude Certified Architect – Foundations
  • Updated: Jul 24, 2026
  • Q & A: 62 Questions and Answers
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Anthropic CCAR-F Exam Syllabus Topics:
SectionWeightObjectives
Claude Code Configuration & Workflows20%- Claude Code usage and configuration
- Integrating Claude Code into development processes
- Developer productivity workflows
Agentic Architecture & Orchestration27%- Designing agentic systems and workflows
- Agent coordination and orchestration patterns
- Selecting appropriate Claude architectures
Tool Design & MCP Integration18%- Designing effective tools for Claude applications
- Model Context Protocol (MCP) concepts and integration
- Tool safety, reliability, and usability
Prompt Engineering & Structured Output20%- Prompt design strategies
- Improving Claude response quality and consistency
- Structured output generation and validation
Context Management & Reliability15%- Managing context windows and information flow
- Evaluation and reliability strategies
- Production deployment considerations
Anthropic Claude Certified Architect – Foundations Sample Questions:

1. 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 need to add a date validation check ensuring event dates are in the future. This requires adding a conditional statement to one existing function in a single file.
What is the most appropriate approach?

A) Enter plan mode to analyze how the validation might impact other parts of the reservation flow.
B) Enter plan mode first to create a detailed implementation strategy before making the change.
C) Use direct execution to make the change.
D) Start with extended thinking mode enabled to ensure thorough reasoning about the validation logic.


2. You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer asks the agent to find all callers of a function before removing it. The function is defined in a core library but is also exposed through wrapper modules that rename the function for domain-specific use (e.
g., calculateTax in the library becomes computeOrderTax in the orders module).
What exploration strategy will most reliably identify all callers?

A) Search for the function name in project documentation to understand intended usage patterns and navigate to documented integration points.
B) Use Grep to find all files that import from the library or wrapper modules, then read each file to check whether it uses the function.
C) Read the library and wrapper modules to identify all exposed names for the function, then Grep for each name across the codebase.
D) Use Grep to search for the function's original name across the codebase.


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 wants Claude to follow a detailed code review checklist (8 items covering API changes, test coverage, documentation, security, etc.) when reviewing pull requests. The team also uses Claude extensively for other tasks: writing new features, debugging production issues, and generating documentation. Currently, developers paste the checklist at the start of each review session.
Which approach best addresses this workflow need?

A) Add the checklist to the project's CLAUDE.md file under a "Code Review" section.
B) Configure plan mode as the default for code review sessions.
C) Create a /review slash command containing the checklist, invoked when starting reviews.
D) Create a dedicated review subagent with the checklist embedded in its configuration.


4. You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
During a billing dispute resolution, your agent successfully retrieves customer info via get_customer and order details via lookup_order , but when attempting to call process_refund , the tool returns a timeout error.
The agent has enough information to explain the charges and verify refund eligibility, but cannot actually process the refund due to the backend failure.
What approach best balances first-contact resolution with appropriate error handling?

A) Implement automatic retries with exponential backoff for process_refund , keeping the conversation open until the refund is successfully processed.
B) Escalate immediately to a human agent since the refund action cannot be completed.
C) Explain the billing, confirm refund eligibility, acknowledge the system issue preventing immediate processing, and offer escalation or retry later.
D) Confirm the refund will be processed and close the conversation, since the system has all necessary information to complete it automatically.


5. 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 extracts event metadata (date, location, organizer, attendee_count) from news articles using a JSON schema with all nullable fields. During evaluation, you observe the model frequently generates plausible but incorrect values for fields not mentioned in the article-for example, outputting "500" for attendee_count when the source contains no attendance information.
What's the most effective way to reduce these false extractions?

A) Make all schema fields required (non-nullable) with strict validation rules to ensure the model only outputs verifiable data.
B) Upgrade to a more capable model tier with improved instruction-following to reduce hallucination tendencies.
C) Add prompt instructions to return null for any field where information is not directly stated in the source.
D) Add a post-processing step using a second LLM call to verify each extracted value exists in the source document.


Solutions:

Question # 1
Answer: C
Question # 2
Answer: C
Question # 3
Answer: C
Question # 4
Answer: C
Question # 5
Answer: C

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