CCAR-P · exam guide v1.0 · effective 2026-07

Claude Certified Architect – Professional

Exam blueprint and preparation reference

The certification is intended for mid- to senior-level technical professionals who design, build, and deliver production-grade AI solutions using large language models, particularly Claude.

About this certification

The Claude Certified Architect – Professional certification validates that an individual can design, build, and deliver production-grade AI solutions using Anthropic's Claude platform. It is intended for practitioners working in an architect role who select appropriate models, architectures, and API patterns; apply prompt and context engineering; integrate Claude into enterprise systems; and incorporate evaluation, security, compliance, and governance considerations into their designs.

Anthropic's words, from section 1 of the CCAR-P exam guide, v1.0. This guide is subject to change without notice.

No practice items for this credential yet.

Its blueprint is fully transcribed, so everything on this page is current — but there is nothing to drill until an original item bank is written against these objectives. Practice is live for CCAR-F.

Exam content outline

Domain names and weights are the guide's. “Expected items” is weight × 63 — what a real sitting draws, not a prediction about anyone's score.

Domain	Weight	Expected items
D1 Solution Design & Architecture	17%	11
D2 Claude Models, Prompting & Context Engineering	13%	8
D3 Integration	19%	12
D4 Evaluation, Testing & Optimization	16%	10
D5 Governance, Safety & Risk Management	14%	9
D6 Stakeholder Communication & Lifecycle Management	14%	9
D7 Developer Productivity & Operational Enablement	7%	4

Objectives, by domain

This guide publishes a flat list of objectives per domain rather than numbered task statements. Exam items are written against these.

D1 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)

D2 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)

D3 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

D4 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

D5 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)

D6 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)

D7 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

Domain names, weights and the objective text above are Anthropic's words, transcribed from section 6 of the CCAR-P exam guide.

Exam details and policies

Form: 63 items · 120 minutes multiple-choice, multiple-response
Fee: $175
Cut score: 720 of 1000 Scaled from a standard-setting study, so it is not 72%. No practice percentage converts into it.
Valid for: 12 months
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

Prerequisites

There are no mandatory prerequisites or courses required to sit this exam. The experience above is recommended, not required. The credential is awarded based on exam performance alone.

Link the company email domain before you book.

Linking takes 7–10 days, and a certification earned on an unlinked domain does not credit toward the Claude Partner Network Services Track count. This is our programme note, not Anthropic exam-guide content — see docs/partner-status.md.