Anthropic CCAR-P Q&A - in .pdf

  • CCAR-P pdf
  • Exam Code: CCAR-P
  • Exam Name: Claude Certified Architect - Professional
  • Updated: Aug 28, 2026
  • Q & A: 116 Questions and Answers
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  • Exam Code: CCAR-P
  • Exam Name: Claude Certified Architect - Professional
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  • Q & A: 116 Questions and Answers
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  • Exam Code: CCAR-P
  • Exam Name: Claude Certified Architect - Professional
  • Updated: Aug 28, 2026
  • Q & A: 116 Questions and Answers
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Anthropic CCAR-P Exam Syllabus Topics:
SectionWeightObjectives
Evaluation, Testing & Optimization16%- Evaluation metrics and datasets
- Cost and performance optimization
- Production monitoring and optimization
- Evaluation framework design
- A/B testing
- System issue diagnosis
Governance, Safety & Risk Management14%- AI safety and guardrails
- Security and risk management
- Human-in-the-loop validation
- Ethical AI considerations
- Regulatory and compliance requirements
Claude Models, Prompting & Context Engineering13%- Prompt reuse and context engineering strategies
- Context window optimization
- Claude model selection and trade-offs
- Guardrails
- System prompts and prompt templates
Solution Design & Architecture17%- Multi-agent systems and orchestration
- Decomposition techniques for complex problem solving
- Architectural patterns
  • 1. Agentic architectures
    • 2. Workflow architectures
      • 3. Augmented LLM architectures
        - Alignment with business value, cost, performance, and SLAs
        - End-to-end architecture design
        - Translating business problems into Claude-based AI solutions
        Integration19%- Enterprise system integration
        - Claude integration mechanisms
        • 1. Agent-to-agent integration
          • 2. MCP
            • 3. API and CLI
              - Authentication and authorization analysis
              - RAG pipeline design
              • 1. Indexing
                • 2. Retrieval
                  • 3. Chunking
                    Stakeholder Communication & Lifecycle Management14%- Discovery and requirements gathering
                    - Stakeholder management
                    - Solution lifecycle management
                    - Service-level agreements
                    - Communicating architectural decisions
                    - Architecture documentation
                    Developer Productivity & Operational Enablement7%- Claude tooling configuration for teams
                    - Developer enablement
                    - AI-assisted developer workflows
                    - Debugging and operational issue resolution
                    Anthropic Claude Certified Architect - Professional Sample Questions:

                    Question 1

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


                    Question 2

                    You are designing a test strategy for a Claude-based pipeline that handles sensitive financial data.
                    Which two test types should be prioritized to cover both safety under attack and cross-component correctness? (Select two.)

                    A. Unit tests targeting only individual prompt-template rendering logic.
                    B. Integration tests that verify end-to-end pipeline behavior across all components.
                    C. Adversarial tests using prompt-injection and malformed-input cases.
                    D. Regression tests against a stable reference set of previously known-good outputs.
                    E. Smoke tests that verify core paths after each deployment.


                    Question 3

                    A managed agent deployment for claims triage has grown from 6 tools to 34 tools over 18 months as product teams added capabilities. Triage accuracy has declined from 91 percent to 78 percent, and average tool- selection latency has increased by 2.3 seconds. A junior engineer has proposed adding a tool-router agent in front of the current agent to filter the tool list per request.
                    Which two findings should you present to justify capability decomposition before adding the router? (Select two.) Each correct answer presents part of the solution.

                    A. Several tools have not been invoked across the most recent 90 days of traffic.
                    B. The 34 tools serve four distinct claim-workflow domains within the triage scope.
                    C. The proposed router introduces an additional model call on every incoming request.
                    D. Tool descriptions overlap across multiple claim categories within the agent's tool set.
                    E. The router pattern is well documented across publicly available agent literature.


                    Question 4

                    You are auditing a procurement-assistant agent whose defined responsibility is to draft purchase requests for review.
                    For each tool currently configured on the agent, select yes if the tool should remain after a least-privilege audit. Otherwise, select no if it should be removed.


                    Question 5

                    The platform team at Trenova Systems, Inc. needs to reduce per-query cost and p95 latency for a high-volume Claude pipeline without degrading output quality on the core use case.
                    Which two optimizations directly target both cost and latency simultaneously? (Select two.)

                    A. Add a retrieval step that fetches the full source corpus into the context window before generation.
                    B. Expand the system prompt to include additional few-shot examples on every request.
                    C. Enable prompt caching on the static system-prompt prefix to reduce billable input tokens on repeated calls.
                    D. Increase max_tokens to reduce the frequency of truncated responses requiring follow-up calls.
                    E. Route straightforward query types to a smaller, faster Claude model and reserve the full model for complex cases.


                    Solutions:

                    Question 1
                    Answer: D
                    Question 2
                    Answer: B,C
                    Question 3
                    Answer: B,C
                    Question 4
                    Answer: Only visible for members
                    Question 5
                    Answer: C,E

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