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Anthropic CCAR-P Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integration | 19% | - Design authentication, authorization, and observability - Integrate Claude with enterprise systems, APIs, and tools - Integrate with data pipelines and RAG systems - Implement Model Context Protocol (MCP) integrations |
| Topic 2: Evaluation, Testing & Optimization | 16% | - Implement iterative improvement pipelines - Define evaluation metrics and success criteria - Optimize performance, prompting, and model selection - Test accuracy, reliability, latency, and cost |
| Topic 3: Solution Design & Architecture | 17% | - Design end-to-end architectures and feedback loops - Translate business problems into Claude-based AI solutions - Align solutions to business value pillars - Design multi-agent systems and orchestration strategies - Select architectural patterns: workflow, agentic, augmented LLM |
| Topic 4: 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 |
| Topic 5: 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 |
| Topic 6: Governance, Safety & Risk Management | 14% | - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Address ethical AI considerations and bias mitigation - Implement guardrails and safety controls - Manage data privacy and security compliance |
| Topic 7: Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Select appropriate Claude models based on trade-offs - Apply context engineering and context management techniques - Design system prompts, templates, and guardrails |
Anthropic Claude Certified Architect - Professional Sample Questions:
A healthcare organization is evaluating two Claude-powered AI architectures for a clinical documentation assistant. Architecture X produces higher output quality scores but costs $0.18 per documentation session and averages 4.2 seconds per response. Architecture Y scores slightly lower on quality metrics but costs $0.09 per session and averages 2.1 seconds per response. The stated SLA requires responses under 3 seconds, and the annual volume is projected at 2 million documentation sessions.
Which evaluation approach correctly applies business value pillar analysis to this decision?
- A. Recommend a hybrid approach using Architecture X for complex cases and Architecture Y for routine cases, without additional analysis, since this preserves quality where it matters most.
- B. Eliminate Architecture X on SLA grounds, then evaluate Architecture Y against the efficiency and solution cost pillars by calculating annual cost difference and assessing whether the quality delta materially impacts clinical workflow productivity.
- C. Select Architecture X because the higher quality scores justify the cost premium, and any SLA gap can be addressed through infrastructure optimization after deployment.
- D. Select Architecture Y based solely on the 50% cost reduction, since solution cost is the most important value pillar in healthcare budget-constrained environments.
Correct Answer: B π³οΈ
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You are reviewing instrumentation in a multi-agent system.
Which two findings constitute valid observability gaps in the instrumentation? (Select two.) Each correct answer presents a complete solution.
- A. Model identity and version on each turn are not recorded with the turn artifacts.
- B. Request-scoped correlation identifiers do not propagate across agent and tool calls.
- C. Tool-call payloads and outcomes are recorded with redaction applied to known sensitive fields.
- D. Latency and token usage on every span are emitted to the central metrics pipeline.
- E. Trace spans for each agent step are exported to the shared distributed-tracing backend.
Correct Answer: A,B π³οΈ
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An engineer inadvertently commits an API key to the repository by placing it in .claude/settings.json.
Which configuration design principle was violated?
- A. Project-scope configuration should always override user-scope configuration.
- B. Credentials must be resolved at runtime from a secret store and never stored in version-controlled files.
- C. Managed configuration must be applied before project-scope configuration is read.
- D. MCP server definitions must reside in environment variables to prevent scope conflicts.
Correct Answer: B π³οΈ
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You are transitioning a Claude-based deployment from design into implementation.
Which handoff package most directly supports a clean transition?
- A. The most recent set of design presentation slides without component-level diagrams, interface contracts, an evaluation framework with a reference set, runbooks, or a known-limitations register.
- B. Architecture overview, ADRs, component contracts, evaluation framework with reference set, runbooks, on-call playbook, and known limitations.
- C. A verbal walkthrough conducted on the day of handoff with no written architecture overview, ADRs, component contracts, evaluation framework, runbooks, playbook, or known limitations.
- D. Source code alone with no integrating architecture overview, ADRs, component contracts, evaluation framework, runbooks, on-call playbook, or known-limitations register to support the delivery team.
Correct Answer: B π³οΈ
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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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