What Is the Claude Certified Architect – Foundations (CCA-F)?
The Claude Certified Architect – Foundations (CCA-F) is Anthropic's official professional certification for practitioners who design and deploy production-scale Claude applications — particularly agent-based and code-centric systems. It validates your ability to architect reliable, scalable AI workflows using Claude's full ecosystem, including the Claude API, Claude Code, and the Model Context Protocol (MCP).
Exam At a Glance
| Detail | Info |
|---|
| Full Name | Claude Certified Architect – Foundations (CCA-F) |
| Administered by | Anthropic (online, proctored) |
| Format | 60 scenario-based multiple-choice questions |
| Duration | 120 minutes |
| Passing Score | 720 out of 1,000 |
| Target Audience | Solution/AI architects, developers with 6+ months of hands-on Claude experience |
| Pricing | ~$99 per attempt (free for Anthropic Partner Network members) |
| Access | Anthropic Skilljar portal |
The Five Exam Domains
Domain 1 — Agentic Architecture & Orchestration (27%)
This is the largest domain and the backbone of the exam. It tests your ability to design and manage agentic loops, multi-agent systems, and complex orchestration patterns.
Key topics:
- Agentic loop lifecycle: sending requests, inspecting
stop_reason (tool_use vs end_turn), executing tools, and returning results
- Model-driven decisions vs. pre-configured decision trees
- Hub-and-spoke coordinator–subagent architecture: a central coordinator manages all sub-agent communication, error handling, task decomposition, and result aggregation
- Sub-agents operating with isolated context (not inheriting the coordinator's full history)
- Multi-step workflow enforcement: programmatic hooks, prerequisite gates, and lifecycle callbacks
- Task decomposition: fixed sequential pipelines vs. adaptive dynamic decomposition
- Investigation plans that generate new subtasks as the agent discovers information
Domain 2 — Tool Design & MCP Integration (18%)
Focuses on how tools are designed, secured, and integrated via the Model Context Protocol (MCP).
Key topics:
- Defining tools with clear boundaries, resources, and validations
- Writing precise tool descriptions that prevent misrouting and ambiguity
- When to split vs. consolidate tools (purpose-specific interfaces vs. monolithic tools)
- Scoped tool access: limiting agents to role-relevant tools (least-privilege principles)
- Structured error responses:
isError flag, error categories (transient, validation, business, permission)
isRetryable semantics and retry-decision logic- Ensuring tool results are appended correctly into conversation history
Domain 3 — Claude Code Configuration & Workflows (20%)
Tests your ability to configure and automate Claude Code-based workflows in CI/CD environments.
Key topics:
CLAUDE.md configuration and Agent Skills- Plan mode and slash commands (
/plan, /tools, /context)
- Pre- and post-run commands
- Integrating Claude Code into CI/CD pipelines (testing, promoting, versioning skills and agents)
- Multi-workspace workflows and command bundling
- Blending editor-driven and agent-driven steps in production environments
Domain 4 — Prompt Engineering & Structured Output (20%)
Covers designing prompts and schemas that reliably drive structured, machine-consumable outputs.
Key topics:
- Context engineering: role-setting, step-by-step instructions, constraints, and guardrails
- JSON-schema-based structured output using
tool_use-style responses
- Few-shot prompting, extraction patterns, and explicit criteria
- Validation-retry loops
- Batch API usage for high-volume structured extraction
- Idempotent, deterministic output formats for downstream processing
Domain 5 — Context Management & Reliability (15%)
Focuses on managing long-context constraints, multi-agent handoffs, and error propagation in production systems.
Key topics:
- Long-context handling: summarization, truncation, and selective injection strategies
- Multi-agent handoffs: preserving context integrity when switching agents or roles
- Escalation triggers: user requests for humans, policy gaps, inability to make meaningful progress
- Ambiguity resolution patterns and escalation to human review
- Confidence calibration, retry strategies, and monitoring
- Contextual safeguards to prevent lossy state or circular loops
Exam Scenarios
All 60 questions are scenario-based — no trivia-style fact recall. During the exam, you'll encounter 4 randomly selected scenarios from a pool of 6:
- Customer Support Agent
- Code Generation with Claude Code
- Multi-Agent Research System
- Developer Productivity Assistant
- Claude Code for CI/CD
- Structured Data Extraction
Each scenario tests multiple domains simultaneously, with the heaviest emphasis on Agentic Architecture & Orchestration, Tool Design & MCP, and Prompt Engineering & Structured Output.
How to Prepare
Recommended Study Path
For practitioners with 6+ months of Claude experience: ~15–20 hours of focused study
For developers new to Claude: ~30–40 hours, including hands-on labs
Free Resources
- claudecertificationguide.com — Community-run guide with 30 lessons, 150+ practice questions, and a full mock exam
- Panaversity CCA-F — 13 free courses explicitly mapped to CCA-F domains
- Vizuara AI Pods — Free hands-on notebooks covering all five domains plus a 60-question practice exam
- Anthropic's official practice exam — Available via the Skilljar portal
Paid Resources
- ExamPro — "Claude Architect Foundations" course aligned to 2025 exam domains
- CertSafari — 600+ exam-style questions in 60-question blocks
Domain Priority Strategy
If you're short on time, focus in this order:
- Domain 1 (27%) — Largest domain; underpins how agents and tools interact
- Domain 3 + Domain 4 (40% combined) — Claude Code and Prompt Engineering together form the biggest combined weight
- Domain 2 (18%) — Direct hands-on knowledge of MCP servers, tool shapes, and error patterns
- Domain 5 (15%) — Context management and reliability patterns
Final Thoughts
The CCA-F is a rigorous, scenario-driven exam that rewards practitioners who have actually built production Claude applications. It's not a memorization test — it's a design test. The best preparation is hands-on experience building agentic workflows, configuring Claude Code, and designing MCP-compliant tools.
If you're an AI architect, solutions engineer, or senior developer working with Claude, this certification is a strong signal of your ability to design reliable, production-grade AI systems.
Ready to get started? Request access at anthropic.skilljar.com.
