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  • Exam Code: CCAR-P
  • Exam Name: Claude Certified Architect - Professional
  • Updated: Aug 24, 2026
  • Q & A: 116 Questions and Answers
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  • Exam Code: CCAR-P
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Anthropic CCAR-P Exam Syllabus Topics:

SectionWeightObjectives
Governance, Safety & Risk Management14%- Implement guardrails and safety controls
- Manage data privacy and security compliance
- Ensure regulatory compliance (GDPR, HIPAA, etc.)
- Address ethical AI considerations and bias mitigation
Claude Models, Prompting & Context Engineering13%- Select appropriate Claude models based on trade-offs
- Design system prompts, templates, and guardrails
- Apply context engineering and context management techniques
- Mitigate prompt injection, leaks, and jailbreak risks
Integration19%- Integrate with data pipelines and RAG systems
- Design authentication, authorization, and observability
- Integrate Claude with enterprise systems, APIs, and tools
- Implement Model Context Protocol (MCP) integrations
Evaluation, Testing & Optimization16%- Optimize performance, prompting, and model selection
- Define evaluation metrics and success criteria
- Implement iterative improvement pipelines
- Test accuracy, reliability, latency, and cost
Developer Productivity & Operational Enablement7%- Improve developer workflows with AI-assisted tooling
- Configure Claude tools and environments for teams
- Support debugging, monitoring, and operational resolution
Stakeholder Communication & Lifecycle Management14%- Communicate architectural decisions and trade-offs
- Manage stakeholder feedback and expectation alignment
- Document architectures and support full lifecycle phases
- Conduct structured discovery and requirement gathering
Solution Design & Architecture17%- Align solutions to business value pillars
- Design end-to-end architectures and feedback loops
- Design multi-agent systems and orchestration strategies
- Translate business problems into Claude-based AI solutions
- Select architectural patterns: workflow, agentic, augmented LLM

Anthropic Claude Certified Architect - Professional Sample Questions:

1. You are supporting an EU-based deployment with GDPR obligations.
Which combination of measures best supports the deployment's GDPR posture?

A) enterprise-tier deployment with a signed Data Processing Addendum, defined data-retention configuration, redaction of personal data not needed for the task, and documented data-subject-rights handling
B) disabling all data-retention configuration, redaction controls, and data-subject-rights handling to simplify day-to-day operations, accepting the resulting GDPR compliance exposure
C) pasting full EU personal data into every prompt to "give Claude complete context" without considering purpose limitation, data minimization, or the organization's Data Processing Addendum obligations
D) using a personal Claude account tier for processing EU personal data at scale, with no signed Data Processing Addendum and no documented data-subject-rights handling procedure


2. You are integrating Claude Code into the team's pull-request workflow. The team wants AI-assisted review without removing human approval.
Which integration design best fits this requirement?

A) Claude Code disables all existing branch-protection rules to streamline the merge process, removing human approval as a required gate.
B) Claude Code silently deletes pull requests it assesses as low quality without posting a comment or notifying the author.
C) Claude Code reviews the pull request and posts a structured analysis as a comment, while a human reviewer retains the approval decision under the existing branch-protection rules.
D) Claude Code merges every pull request automatically after completing its analysis, bypassing human approval and the existing branch-protection rules.


3. You are an architect supporting the iteration phase of a deployed Claude-based system.
Which activity most directly fits this phase?

A) Rebuild the entire system architecture from scratch at the start of every iteration cycle regardless of what production telemetry, evaluations, and stakeholder feedback indicate is needed.
B) Review production telemetry, sampled output evaluations, and stakeholder feedback to identify the highest-impact change for the next cycle, then plan the change against the evaluation framework.
C) Discard the evaluation framework and reference set once the deployment is in production, accepting that future iterations will have no structured basis for measuring the impact of changes.
D) Stop measuring production outcomes once the deployment has successfully launched, treating the go- live milestone as the end of the evaluation and iteration cycle.


4. A loan pre-qualification assistant shows 94 percent approval recommendations that match the human underwriter decision. The fairness team has reviewed approval rate parity across protected groups and reported no significant difference. A board member has asked whether this evidence is sufficient to declare the assistant fair.
Which two Discernment-competency findings should you report? (Select two.) Each correct answer presents part of the solution.

A) The 94 percent match rate is sufficient evidence of fairness for the assistant's decisions.
B) A larger sample is needed before any meaningful fairness claim can be made about the model.
C) The fairness team's review process likely missed at least some of the protected groups studied.
D) Approval rate parity does not by itself assess error rate parity across protected groups.
E) Match with human underwriters does not establish freedom from underwriter-introduced bias.


5. You are integrating AI-assisted tooling into the team's documentation workflow. The team wants generated documentation that stays grounded in the actual code.
Which integration approach best fits this requirement?

A) Configure subagents that read the relevant code files via filesystem and code-search tools, generate the documentation, and emit changes through the team's normal review workflow.
B) Have the subagents publish generated documentation directly to the public-facing site without passing through the team's normal review workflow or any human approval step.
C) Disable all filesystem and code-search tools so the subagents cannot read any repository code, accepting that documentation generation will be entirely disconnected from the actual implementation.
D) Generate documentation from the model's training-data recall without reading any of the actual repository code, accepting that the output will not reflect the current implementation.


Solutions:

Question # 1
Answer: A
Question # 2
Answer: C
Question # 3
Answer: B
Question # 4
Answer: D,E
Question # 5
Answer: A

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