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Anthropic CCAR-P Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Solution Design & Architecture | 17% | - Design end-to-end architectures and feedback loops - Align solutions to business value pillars - Design multi-agent systems and orchestration strategies - Translate business problems into Claude-based AI solutions - Select architectural patterns: workflow, agentic, augmented LLM |
| Claude Models, Prompting & Context Engineering | 13% | - Design system prompts, templates, and guardrails - Mitigate prompt injection, leaks, and jailbreak risks - Select appropriate Claude models based on trade-offs - Apply context engineering and context management techniques |
| Developer Productivity & Operational Enablement | 7% | - Configure Claude tools and environments for teams - Support debugging, monitoring, and operational resolution - Improve developer workflows with AI-assisted tooling |
| Integration | 19% | - Integrate Claude with enterprise systems, APIs, and tools - Implement Model Context Protocol (MCP) integrations - Design authentication, authorization, and observability - Integrate with data pipelines and RAG systems |
| Governance, Safety & Risk Management | 14% | - Address ethical AI considerations and bias mitigation - Manage data privacy and security compliance - Implement guardrails and safety controls - Ensure regulatory compliance (GDPR, HIPAA, etc.) |
| Evaluation, Testing & Optimization | 16% | - Implement iterative improvement pipelines - Test accuracy, reliability, latency, and cost - Optimize performance, prompting, and model selection - Define evaluation metrics and success criteria |
| Stakeholder Communication & Lifecycle Management | 14% | - Document architectures and support full lifecycle phases - Conduct structured discovery and requirement gathering - Manage stakeholder feedback and expectation alignment - Communicate architectural decisions and trade-offs |
Anthropic Claude Certified Architect - Professional Sample Questions:
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. 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.
- C. 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.
- D. 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.
Correct Answer: A 🗳️
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You are a platform architect designing an internal Claude-based assistant that serves both finance analysts and external auditors. Each population must access only documents permitted by its role.
Where should role-based access control be enforced in the pipeline?
- A. At the retrieval layer, before any role-restricted content reaches the prompt-construction step or the model.
- B. Nowhere in the pipeline; rely on the model's general refusal behavior to reject unauthorized document access without any enforced access control.
- C. Inside the system prompt as a natural-language instruction for Claude to ignore unauthorized documents.
- D. After the response is generated, by post-filtering content that should not have been retrieved.
Correct Answer: A 🗳️
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You are building a feedback-and-alignment routine for a multi-stakeholder deployment.
Which two practices belong in the routine? (Select two.)
Each correct answer presents a complete solution.
- A. Reconcile divergent stakeholder positions explicitly rather than papering over them in the moment.
- B. Rotate facilitation among the participating stakeholder groups to share ownership of the routine.
- C. Escalate any disagreement among stakeholders to the executive sponsor for binding resolution.
- D. Set a regular cadence for revisiting expectations and assumptions as conditions evolve over time.
- E. Record each session and distribute the recording to stakeholders who could not attend live.
Correct Answer: A,D 🗳️
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You are an architect supporting the iteration phase of a deployed Claude-based system.
Which activity most directly fits this phase?
- A. 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.
- B. Stop measuring production outcomes once the deployment has successfully launched, treating the go- live milestone as the end of the evaluation and iteration cycle.
- C. 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.
- D. 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.
Correct Answer: D 🗳️
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You are sequencing decomposed components in an invoice-processing pipeline.
For each of the decomposed components, select the execution layer it belongs to: "Pre-Processing," "Model Stage," or "Post-Processing."
Correct Answer:

Explanation:
Model Stage, Post-Processing, Post-Processing, Model Stage, Pre-Processing, Pre-Processing Pre-processing prepares source material before Claude receives it. Optical character recognition converts scanned invoice images into machine-readable text. Because PII redaction is explicitly performed before model invocation, it also belongs in pre-processing. Additional activities at this layer can include file validation, malware scanning, normalization, page separation, and metadata extraction.
The model stage contains tasks requiring Claude's language-understanding capability. Classifying the invoice type involves interpreting textual and contextual features, while extracting structured invoice fields requires mapping unstructured content into defined business attributes.
Post-processing verifies and commits the model's result. Schema validation confirms that required fields exist, data types are correct, enumerated values are permitted, and structural constraints are satisfied.
Persistence must occur only after validation and any required human review because the system of record should not receive malformed or unapproved model output.
This decomposition separates probabilistic inference from deterministic processing. OCR, redaction, validation, and persistence do not need to be delegated to Claude when conventional components can execute them more predictably. The resulting architecture reduces model workload, strengthens privacy controls, improves auditability, and prevents unvalidated output from directly changing authoritative records.
Study Guide references/topics: Task decomposition; pre-processing; model inference; post-processing; deterministic validation; privacy-by-design; system-of-record protection.
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