Anthropic : CCAR-P Questions & Answers
Last Updated: Sep 08, 2026
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Anthropic CCAR-P Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Integration | 19% | - Implement Model Context Protocol (MCP) integrations - Integrate Claude with enterprise systems, APIs, and tools - Design authentication, authorization, and observability - Integrate with data pipelines and RAG systems |
| Evaluation, Testing & Optimization | 16% | - Implement iterative improvement pipelines - Define evaluation metrics and success criteria - Test accuracy, reliability, latency, and cost - Optimize performance, prompting, and model selection |
| Solution Design & Architecture | 17% | - Design multi-agent systems and orchestration strategies - Translate business problems into Claude-based AI solutions - Select architectural patterns: workflow, agentic, augmented LLM - Align solutions to business value pillars - Design end-to-end architectures and feedback loops |
| Stakeholder Communication & Lifecycle Management | 14% | - Communicate architectural decisions and trade-offs - Manage stakeholder feedback and expectation alignment - Conduct structured discovery and requirement gathering - Document architectures and support full lifecycle phases |
| Governance, Safety & Risk Management | 14% | - Implement guardrails and safety controls - Address ethical AI considerations and bias mitigation - Manage data privacy and security compliance - Ensure regulatory compliance (GDPR, HIPAA, etc.) |
| Developer Productivity & Operational Enablement | 7% | - Configure Claude tools and environments for teams - Improve developer workflows with AI-assisted tooling - Support debugging, monitoring, and operational resolution |
| Claude Models, Prompting & Context Engineering | 13% | - Design system prompts, templates, and guardrails - Select appropriate Claude models based on trade-offs - Mitigate prompt injection, leaks, and jailbreak risks - Apply context engineering and context management techniques |
Anthropic Claude Certified Architect - Professional Sample Questions:
A security audit uncovers two issues: (1) all end users share a single API key, and (2) tool calls are executed without logging the initiating user.
Which two mitigations directly address these specific findings? (Select two.)
- A. Move credentials out of the prompt context and resolve them from a server-side secret store.
- B. Enforce RBAC at the retrieval layer before content enters the model context.
- C. Replace the shared API key with per-user OAuth tokens carrying scope-restricted permissions.
- D. Validate structured outputs against a schema before downstream actions are executed.
- E. Add actor attribution to tool-call logs so each call records the initiating user identity.
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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. A larger sample is needed before any meaningful fairness claim can be made about the model.
- B. The fairness team's review process likely missed at least some of the protected groups studied.
- C. Match with human underwriters does not establish freedom from underwriter-introduced bias.
- D. The 94 percent match rate is sufficient evidence of fairness for the assistant's decisions.
- E. Approval rate parity does not by itself assess error rate parity across protected groups.
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You are rolling out a standardized Claude Code configuration to an engineering team and must complete the planning steps before piloting the configuration.
Which two steps must be completed BEFORE piloting the configuration with a small group of engineers?
(Select two.)
Each correct answer presents part of the solution.
- A. Onboard every engineer in the organization to the new configuration through mandatory training sessions.
- B. Iterate the configuration based on the pilot findings and stabilize the baseline before broader rollout.
- C. Identify the team workflows, security boundaries, and which decisions belong to managed configuration versus project scope.
- D. Define the project-scope baseline covering Model Context Protocol (MCP) servers, permission rules, and subagents.
- E. Roll out the stabilized configuration to additional teams with documentation and a defined support channel.
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You are identifying signals that a deployment should re-enter design rather than continue iterating in place.
Which signal most directly indicates the need for a new design cycle?
- A. A dashboard alert threshold needs a small numerical adjustment to reduce false-positive noise, which can be handled as an operational configuration change without a new design cycle.
- B. A runbook step requires a clarification edit to improve on-call guidance accuracy, which can be handled as a documentation update without changes to component responsibilities or core contracts.
- C. The system's current architecture cannot meet the new requirements without changes to component responsibilities or core contracts.
- D. A minor copy edit is requested in a customer-facing string within the existing UI, which can be handled as a localized content change without altering component responsibilities or contracts.
You are selecting a pattern for a compliance Q & A assistant that must answer policy questions with citations to the authoritative internal source set. Latency, cost, and audit predictability are prioritized.
Which pattern is the best fit?
- A. Augmented LLM with retrieval-augmented generation over the indexed authoritative corpus and citation rendering on each answer.
- B. Multi-agent orchestration with a planner, researcher, and writer agent for every query.
- C. A pure agent loop with open web-browsing tools to surface the most current policy information, without constraining retrieval to the authoritative internal corpus.
- D. A static prompt with the entire policy corpus concatenated into every request.
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