NEW Claude Certified Architect – Professional (CCAR-P) EXAMS

Prepare to pass the official Claude Certified Architect – Professional (CCAR-P) certification exam with these comprehensive, scenario-based practice tests. Designed to strictly match the official Anthropic exam blueprint (Version 1.0), this practice course helps you validate your ability to design, build, and deliver production-grade AI solutions using the Claude platform.

Every question reflects the cognitive level and style of the real assessment—evaluating your real-world decision-making regarding model selection, prompt caching, multi-agent orchestration, RAG pipeline design, evaluation metrics, and enterprise security.

Exam Specifications & Details

• Exam Code: CCAR-P

• Format: Multiple-choice and multiple-response items (item states exact number of responses to select)

• Number of Questions: 63 questions

• Time Limit: 120 minutes

• Passing Score: Scaled score of 720 (on a scale of 100–1,000)

• Exam Fee: $175 USD

• Certification Validity: 12 months from the date awarded

• Delivery Method: Online proctored or in-person at a Pearson VUE test center

• Languages: English

• Result Reporting: Pass/Fail status with scaled score and percent-correct breakdown per domain

Official Exam Domain Breakdown

This practice course accurately measures your knowledge across all 7 official content domains:

• Domain 1: Solution Design & Architecture (17%) – Multi-agent systems, orchestration strategies, workflow design, and business value mapping.

• Domain 2: Claude Models, Prompting & Context Engineering (13%) – Model selection, zero/few-shot prompting, chain-of-thought, prompt caching, and context window optimization.

• Domain 3: Integration (19%) – Tool configuration, least-privilege access, accuracy-latency trade-offs, RAG chunking/indexing, and Model Context Protocol (MCP) integrations.

• Domain 4: Evaluation, Testing & Optimization (16%) – Evaluation frameworks, A/B testing, diagnosing prompt failures/hallucinations, and token/cost optimization.

• Domain 5: Governance, Safety & Risk Management (14%) – Guardrails, human-in-the-loop validation, risk mitigation, and compliance standards (GDPR, HIPAA, FedRAMP).

• Domain 6: Stakeholder Communication & Lifecycle Management (14%) – Requirement discovery, architectural documentation, and lifecycle support.

• Domain 7: Developer Productivity & Operational Enablement (7%) – Configuring environment tools like Claude Code and optimizing developer workflows.

What Will Students Learn in Your Course?

After completing this practice course, candidates will be able to:

• Analyze and solve real-world scenario questions structured identically to the CCAR-P exam items.

• Architect end-to-end Claude solutions by choosing optimal models, context strategies, and integration protocols (such as MCP).

• Design secure, high-performing RAG and multi-agent pipelines with appropriate retrieval, chunking, and least-privilege security controls.

• Implement evaluation and optimization techniques to diagnose hallucinations, reduce latency, and control per-request token costs using prompt caching.

• Apply governance, compliance, and safety controls (including human-in-the-loop mechanisms and regulatory guardrails) to production LLM deployments.

Who Is This Course For?

This course is designed for mid- to senior-level technical professionals operating at the intersection of business requirements and technical implementation.

• Solution Architects & AI/ML Engineers building end-to-end systems on Anthropic's Claude.

• Technical Leads & Senior Software Engineers responsible for designing LLM orchestration, RAG architectures, and API integrations.

• Platform Engineers & Cloud Architects implementing security, governance, and operational observability for AI applications.

Recommended Experience

• Foundation in software engineering best practices (modular design, scalability).

• 3+ years of experience in systems architecture or platform engineering.

• 6+ months of hands-on experience deploying Claude or comparable LLM-based systems in production.

(Note: This course is not intended for entry-level developers, non-technical professionals, or casual users of AI tools without end-to-end system design experience.)