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Anthropic CCAR-P Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Stakeholder Communication & Lifecycle Management | 14% | - Discovery and requirements gathering - Stakeholder management - Architecture documentation - Solution lifecycle management - Communicating architectural decisions - Service-level agreements |
| Claude Models, Prompting & Context Engineering | 13% | - Claude model selection and trade-offs - Guardrails - Prompt reuse and context engineering strategies - Context window optimization - System prompts and prompt templates |
| Developer Productivity & Operational Enablement | 7% | - Debugging and operational issue resolution - Developer enablement - AI-assisted developer workflows - Claude tooling configuration for teams |
| Solution Design & Architecture | 17% | - End-to-end architecture design - Alignment with business value, cost, performance, and SLAs - Translating business problems into Claude-based AI solutions - Architectural patterns
- Decomposition techniques for complex problem solving |
| Integration | 19% | - RAG pipeline design
- Claude integration mechanisms
|
| Governance, Safety & Risk Management | 14% | - Human-in-the-loop validation - AI safety and guardrails - Ethical AI considerations - Regulatory and compliance requirements - Security and risk management |
| Evaluation, Testing & Optimization | 16% | - Evaluation framework design - Cost and performance optimization - Evaluation metrics and datasets - System issue diagnosis - Production monitoring and optimization - A/B testing |
Anthropic Claude Certified Architect - Professional Sample Questions:
You are defining an SLA for a Claude-based assistant.
Which SLA definition is most operationally meaningful?
- A. A qualitative commitment such as "the system will be fast and reliable," which names no metric, threshold, or evaluation window.
- B. A target tied to a stakeholder sentiment measure such as "the team feels satisfied," which cannot be measured objectively or used to trigger a documented breach response.
- C. A measurable target with a defined metric, threshold, evaluation window, and consequence for breach- for example, "p95 per-request latency under 800 ms over a 28-day window."
- D. A target that names the metric and threshold but omits the evaluation window and breach consequence, leaving compliance periods and remediation triggers undefined.
Correct Answer: C 🗳️
Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).
You are evaluating prompting claims in a peer's design document.
For each claim, select yes if the claim reflects sound practice. Otherwise, select no.
Correct Answer:
Explanation:
Yes, Yes, No, Yes, No
Zero-shot prompting is a reasonable baseline for a clearly defined closed-set classifier because the model receives both the permissible labels and their meanings. Performance should then be measured against an evaluation set before additional prompt complexity is introduced. Few-shot examples are also sound for extraction because examples containing the exact field names demonstrate the required schema and reduce formatting ambiguity. Anthropic identifies examples as one of the most reliable methods for steering output format, structure, and consistency. Prompting Best Practices Chain-of-thought is not an unconditional default. Additional reasoning is appropriate when the task requires substantive decomposition or complex judgment, but routine classification and extraction may gain little while consuming more tokens and increasing latency. The technique should be selected according to evaluated task requirements.
An even-handed comparison governed by explicit criteria mitigates leading-question bias by requiring symmetrical treatment of alternatives rather than presupposing a preferred conclusion. Finally, prompt validation is model-specific. New model versions may follow instructions differently, change formatting behavior, alter tool selection, or produce different latency and cost characteristics. Anthropic's migration guidance consequently recommends reevaluating prompts and workloads against the new baseline. Model Migration Guide Study Guide references/topics: Zero-shot prompting; few-shot examples; reasoning techniques; neutral framing; model migration and regression evaluation.
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. Roll back to the previous Claude Code version immediately to confirm whether the issue is version specific.
- B. File a support ticket with vendor support before any local reproduction or evidence collection.
- C. Increase the model sampling temperature so that intermittent issues surface more frequently for analysis.
- D. Reproduce the issue with a minimal reproduction case that isolates one variable at a time.
- E. List the configured Model Context Protocol (MCP) servers and inspect server status to identify connection failures.
Correct Answer: D,E 🗳️
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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. Approval rate parity does not by itself assess error rate parity across protected groups.
- C. A larger sample is needed before any meaningful fairness claim can be made about the model.
- D. The fairness team's review process likely missed at least some of the protected groups studied.
- E. Match with human underwriters does not establish freedom from underwriter-introduced bias.
Correct Answer: B,E 🗳️
Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).
You are identifying the highest-impact optimization for a deployment whose token cost is dominated by a long, repeated system prompt and a large retrieved context per request.
Which optimization most directly targets the dominant cost driver?
- A. Add additional repeated content to the system prompt to give the model more guidance.
- B. Switch every request to the heaviest available model to maximize output quality, accepting that higher per-request inference cost compounds rather than addresses the dominant cost driver.
- C. Move the long, repeated system prompt into a cacheable prefix and trim retrieved context to the spans relevant to each query.
- D. Increase retrieval depth on every request to maximize recall, worsening the dominant cost driver by adding more retrieved tokens per request rather than reducing them.
Correct Answer: C 🗳️
Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).
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