Claude Certified Architect – Professional
Mid- to senior-level solution architects, AI/ML engineers, technical leads and senior software engineers who design, build and deliver production-grade AI solutions, and who own architectural decisions including security, compliance and governance.
No study material for this exam yet
Everything below is the official blueprint, so you can see exactly what is tested and in what proportion. We would rather publish nothing than publish practice questions we cannot source — the existing bank for Architect – Foundations cites a documentation line for every single answer, and this exam will get the same treatment or none.
In the meantime, the official guide contains 3 sample questions with answer keys and rationale — Anthropic's own, which we link rather than reproduce. Read the official guide →
Exam details
- Exam code
- CCAR-P
- Number of items
- 63
- Time limit
- 120 minutes
- Passing score
- 720 on a scale of 100–1,000
- Exam fee
- $175 USD
- Validity
- 12 months
- Item format
- Multiple-choice and multiple-response items; each item states how many responses to select
- Delivery
- Proctored: online proctored and/or test center, per program policy
- Result reporting
- Pass/fail with scaled score (100–1,000), plus percent-correct by domain on the score report
Fee is a list price; partner-tier discounts apply automatically at checkout. Registration requires a company email on a domain in the Claude Partner Network.
Blueprint — 7 domains
Weights are the approximate proportion of scored items drawn from each domain.
Solution Design & Architecture
17%- Translate business problems into Claude-based AI solutions
- Design end-to-end architectures (input → processing → output → feedback loops)
- Select appropriate architectural patterns (workflow, agentic, augmented LLM)
- Design multi-agent systems and orchestration strategies
- Apply decomposition techniques for complex problem solving
- Align solutions to business value pillars (efficiency, transformation, productivity, cost, performance SLAs)
Claude Models, Prompting & Context Engineering
13%- Select appropriate Claude models based on trade-offs
- Design system prompts, templates, and guardrails
- Apply prompt engineering techniques (zero-shot, few-shot, chain-of-thought)
- Optimize context windows and manage token usage
- Implement prompt reuse strategies (caching, modular prompts, Skills)
Integration
19%- Evaluate tool/agent configuration for capability bloat
- Analyze authentication and authorization requirements to identify security gaps
- Evaluate accuracy-latency trade-offs and justify configuration decisions
- Analyze observability challenges and select monitoring strategies at scale
- Design a RAG pipeline with appropriate chunking and indexing strategies
- Apply retrieval strategies matched to data shape and query pattern
- Evaluate connection protocols and select the appropriate integration mechanism (MCP, API/CLI, agent-to-agent)
- Evaluate progressive discovery vs. monolithic context strategy
Evaluation, Testing & Optimization
16%- Define evaluation metrics (accuracy, latency, cost, safety, security)
- Design evaluation datasets and test frameworks using mixed methodologies
- Conduct A/B testing and iterative improvements
- Diagnose system issues (prompt failure, hallucinations, model mismatch)
- Optimize token usage, latency, and cost-performance trade-offs
- Monitor system performance using logging and observability tools
Governance, Safety & Risk Management
14%- Implement guardrails and safety controls
- Identify risks, limitations, and failure modes of LLM systems
- Apply human-in-the-loop validation strategies
- Ensure compliance with regulations (e.g., GDPR, HIPAA, FedRAMP)
- Address ethical AI considerations (bias, fairness, transparency)
Stakeholder Communication & Lifecycle Management
14%- Conduct structured discovery and requirement gathering
- Communicate architectural decisions and trade-offs
- Manage stakeholder feedback loops and expectation alignment (including SLAs)
- Document architectures and provide implementation guidance
- Support lifecycle phases (discovery, design, handoff, monitoring, iteration)
Developer Productivity & Operational Enablement
7%- Configure Claude tools and environments for teams (e.g., Claude Code)
- Improve developer workflows using AI-assisted tooling
- Support debugging and operational issue resolution
Official sample questions
The exam guide publishes 3 sample questions with answer keys and rationale. The guide states these are illustrative and not drawn from the live item bank. They are Anthropic's questions, so we link them rather than republish them: open the guide →
Source
Every figure above is from the official Claude Certified Architect – Professional exam guide, v1.0, effective July 2026. Anthropic notes the guide is subject to change without notice; we check it for changes and republish when it moves.
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