CCAR-P Course Overview

Exam code CCAR-P 63 items · 120 min $175 Pass 720/1000

What the credential validates

That you can architect production Claude systems end to end – translating ambiguous business problems into resilient, cost-aware, compliant solutions; selecting and routing between models; designing retrieval and integration layers; standing up evaluation and optimisation programmes; governing safety and regulatory risk; and communicating decisions across their full lifecycle to technical and non-technical stakeholders.

This is the Professional-tier Architect exam. It assumes you already operate at Foundations level and tests senior-architect judgment: trade-offs under constraint, decision matrices, cost models, ADR-style reasoning, and the ability to justify why a design is correct for a specific context rather than reciting features.

Intended for: solutions architects, staff/principal engineers, and technical leads who own Claude system design in an enterprise, including regulated industries.

Not intended for: first-time Claude users, or roles that only consume Claude as a productivity tool (see CCAO-F).

Relationship to CCAR-F (read this first)

CCAR-P is not a disjoint syllabus from the Architect – Foundations exam. Every one of the five CCAR-F domains reappears inside CCAR-P at higher altitude – you are no longer asked what a pattern is, but when to choose it under conflicting constraints and how to defend the choice.

CCAR-F domain	Reappears in CCAR-P as	Altitude shift
Agentic Architecture & Orchestration (27%)	D1 Solution Design & Architecture; multi-agent parts of D3	From “build a coordinator/subagent loop” to “choose workflow vs agentic vs augmented-LLM under SLA, cost and reliability constraints, and write the ADR”
Prompt Engineering & Structured Output (20%)	D2 Models, Prompting & Context Engineering	From “write a good prompt” to “govern prompts as versioned assets, route across a model portfolio, manage breaking changes and caching architecture”
Tool Design & MCP Integration (18%)	D3 Integration (incl. RAG)	From “define a tool schema” to “capability-bloat and least-privilege analysis, identity propagation, MCP vs API vs agent-to-agent selection”
Context Management & Reliability (15%)	D1 HA/fallback; D2 context engineering; D4 optimisation	From “manage the context window” to “capacity planning, rate-limit tiers, fallback design, observability at scale”
Claude Code Configuration & Workflows (20%)	D7 Developer Productivity & Operational Enablement	From “configure CLAUDE.md and settings” to “roll out managed policies and enablement programmes across teams”

The four deltas to study

Four areas are new or substantially deeper at Professional level. If you only have time to close gaps, close these:

1. RAG architecture (D3, 19% – the biggest domain). Chunking strategy by data shape, dense/sparse/hybrid retrieval, reranking, grounding/citations, retrieval evaluation, and the RAG vs long-context vs fine-tuning decision. Almost absent at Foundations.
2. Evaluation frameworks and A/B testing (D4, 16%). Golden sets, LLM-as-judge calibration, pairwise testing with statistical significance, offline vs online evals, regression suites in CI, and structured failure diagnosis.
3. Regulated-industry compliance (D5, 14%). GDPR/DPIA/residency, HIPAA/BAA/PHI, FedRAMP High via Bedrock/Vertex, SOC 2, EU AI Act, Zero Data Retention (and Fable 5.1’s 30-day retention requirement).
4. Stakeholder communication and lifecycle management (D6, 14%). Discovery frameworks, ADRs and decision matrices, C4 documentation, lifecycle phases with exit criteria, and managing model deprecations as a lifecycle event.

Blueprint (Exam Guide v1.0, effective July 2026)

#	Domain	Weight	Items (approx.)	Course page
1	Solution Design & Architecture	17%	~11	Domain 1
2	Claude Models, Prompting & Context Engineering	13%	~8	Domain 2
3	Integration (incl. RAG)	19%	~12	Domain 3
4	Evaluation, Testing & Optimization	16%	~10	Domain 4
5	Governance, Safety & Risk Management	14%	~9	Domain 5
6	Stakeholder Communication & Lifecycle Management	14%	~9	Domain 6
7	Developer Productivity & Operational Enablement	7%	~4	Domain 7

The Architect’s mindset

The exam rewards one posture consistently: an architect chooses under constraint and can defend the choice with evidence. Correct answers tend to:

• Start from business value pillars (efficiency, cost, performance SLAs) and derive the design, not the reverse.
• Prefer programmatic enforcement (hooks, validation, stop_reason) over prompt-based enforcement for critical rules.
• Apply least privilege to tools – remove unneeded capabilities rather than logging or confirming them.
• Design for failure: fallback models, retries with backoff, idempotency, human gates on irreversible actions.
• Measure with per-segment metrics and independent evaluation, never aggregate-only or self-report.
• Match cloud placement and retention to data residency and compliance, not convenience.
• Treat prompts, models and tools as versioned, governed assets with rollout and rollback plans.

Wrong answers tend to: over-engineer (multi-agent where a workflow suffices), trust self-reported confidence, use arbitrary iteration caps or natural-language parsing for control flow, give agents 18 tools “just in case”, optimise a single number while masking per-segment regressions, and skip the DPIA/BAA/human gate because “it is internal”.

Suggested time allocation (35-hour plan)

Domain	Weight	Hours
Integration (incl. RAG)	19%	8
Solution Design & Architecture	17%	6.5
Evaluation, Testing & Optimization	16%	6
Governance, Safety & Risk Management	14%	5
Stakeholder Communication & Lifecycle	14%	5
Models, Prompting & Context Engineering	13%	3
Developer Productivity & Operational Enablement	7%	1.5

Hands-on preparation checklist

• Build a RAG pipeline end to end (ingest → chunk → embed → index → retrieve → rerank → ground) and instrument recall@k and faithfulness.
• Reproduce the confident-but-wrong-after-refresh failure: change a document, watch stale retrieval produce a wrong answer, and fix it at the index layer.
• Write an ADR for a workflow-vs-agentic decision with a decision matrix and rejected alternatives.
• Stand up an LLM-as-judge eval in a separate session/model and calibrate it against human labels.
• Run a pairwise A/B test of two prompts and compute whether the difference is statistically significant.
• Model the cost of routing Haiku 4.5 → Sonnet 5 → Opus 5 with prompt caching and Batch API on a realistic workload.
• Do a capability-bloat / least-privilege review of an agent’s tool set and remove unneeded destructive tools.
• Draft a DPIA outline and a BAA/PHI handling note for a regulated deployment; decide Bedrock vs Vertex vs direct API on residency grounds.
• Produce a C4 context + container view and a runbook for one system.

Course pages