Anthropic CCAR-P Exam Overview:
| Certification Vendor: | Anthropic |
|---|---|
| Exam Name: | Claude Certified Architect - Professional |
| Exam Number: | CCAR-P |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 63 |
| Passing Score: | 720/1000 |
| Certificate Validity Period: | 12 months from the date the credential is awarded |
| Exam Price: | $175 USD |
| Related Certifications: | Claude Certified Architect - Foundations (CCAR-F) |
| Exam Format: | Multiple-response, Multiple-choice |
| Available Languages: | English |
| Sample Questions: | Anthropic CCAR-P Sample Questions |
| Exam Way: | Proctored through Pearson VUE, either online-proctored or at a Pearson VUE test center. |
| Pre Condition: | No prerequisite course, examination, or prior Claude certification is required. Anthropic recommends 3+ years of systems architecture or platform engineering experience and 6+ months of hands-on experience with Claude or comparable LLM systems in production. Registration is currently associated with the Claude Partner Network/Partner Academy. |
| Official Syllabus URL: | https://www.anthropic.com/partners |
Anthropic CCAR-P Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Solution Design & Architecture | 17% | - Decomposition techniques for complex problem solving - Architectural patterns
- Multi-agent systems and orchestration - Translating business problems into Claude-based AI solutions - End-to-end architecture design |
| Stakeholder Communication & Lifecycle Management | 14% | - Service-level agreements - Communicating architectural decisions - Architecture documentation - Solution lifecycle management - Stakeholder management - Discovery and requirements gathering |
| Evaluation, Testing & Optimization | 16% | - System issue diagnosis - Cost and performance optimization - Evaluation metrics and datasets - A/B testing - Production monitoring and optimization - Evaluation framework design |
| Integration | 19% | - Authentication and authorization analysis - Enterprise system integration - Claude integration mechanisms
|
| Developer Productivity & Operational Enablement | 7% | - Claude tooling configuration for teams - Debugging and operational issue resolution - Developer enablement - AI-assisted developer workflows |
| Governance, Safety & Risk Management | 14% | - Human-in-the-loop validation - Regulatory and compliance requirements - Security and risk management - Ethical AI considerations - AI safety and guardrails |
| Claude Models, Prompting & Context Engineering | 13% | - System prompts and prompt templates - Claude model selection and trade-offs - Context window optimization - Guardrails - Prompt reuse and context engineering strategies |
Anthropic Claude Certified Architect - Professional Sample Questions:
Question 1
You are compiling a diagnostic toolkit for Claude Code operational issues.
Which two diagnostic actions belong in the toolkit? (Select two.)
Each correct answer presents a complete solution.
A. File a support ticket with vendor support before any local reproduction or evidence collection.
B. Increase the model sampling temperature so that intermittent issues surface more frequently for analysis.
C. Roll back to the previous Claude Code version immediately to confirm whether the issue is version specific.
D. List the configured Model Context Protocol (MCP) servers and inspect server status to identify connection failures.
E. Reproduce the issue with a minimal reproduction case that isolates one variable at a time.
Question 2
You are operating an interactive assistant whose dominant performance constraint is per-turn latency. Quality on routine turns is already acceptable.
Which configuration adjustment most directly improves latency without disproportionately damaging quality?
A. Increase retrieval depth to the corpus maximum to improve recall regardless of latency.
B. Reduce retrieval depth to the top-k passages that historically cover the answer, and cache stable system- prompt content.
C. Switch every turn to the heaviest available model to maximize output quality, accepting that the increased model latency will worsen the per-turn SLO rather than improve it.
D. Disable prompt caching entirely to ensure fresh context processing on every request, preventing stale prefix content from affecting latency-sensitive interactions.
Question 3
During an architectural review, the security team identifies a risk that adversarial content injected into retrieved documents could manipulate the model's behavior.
Which mitigation most directly addresses this threat?
A. Require citations for each claim and constrain responses to source-supported content.
B. Restrict outbound tool calls to an approved destination allow-list.
C. Treat all retrieved content as untrusted input and apply input classifiers with output validation.
D. Score outputs against a stable adversarial evaluation set on each model-version change.
Question 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. Approval rate parity does not by itself assess error rate parity across protected groups.
B. The 94 percent match rate is sufficient evidence of fairness for the assistant's decisions.
C. A larger sample is needed before any meaningful fairness claim can be made about the model.
D. Match with human underwriters does not establish freedom from underwriter-introduced bias.
E. The fairness team's review process likely missed at least some of the protected groups studied.
Question 5
A senior architect is managing stakeholder expectations for a Claude-based reporting assistant midway through development. Stakeholders have escalating concerns about response latency.
Which two actions most directly address stakeholder expectation alignment in this situation? (Select two.)
A. Present measured p50 and p95 latency baselines against the agreed SLA thresholds so stakeholders have accurate data.
B. Pause all development and reallocate engineering resources entirely to latency optimization.
C. Replace the current Claude model with a third-party model that may offer lower latency without evaluation.
D. Revise the SLA definition collaboratively with stakeholders if current targets are not achievable given production constraints.
E. Communicate that latency concerns are a known LLM limitation and outside the architecture team's control.
Solutions:
| Question 1 Answer: D,E | Question 2 Answer: B | Question 3 Answer: C | Question 4 Answer: A,D | Question 5 Answer: A,D |


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