Anthropic's Claude Certified Architect, Foundations (CCA-F) is their new certification for building production AI systems with Claude. Not prompting quizzes. Not multiple choice trivia. Scenario-based architecture questions that test whether you can actually design multi-agent systems, manage context budgets, and make the right tradeoffs under constraints.
A Redditor (u/Significant-Hornet37) released a full open-source prep guide on GitHub with 33 practice questions, a concepts cheat sheet, and real exam tips. Full credit to them for putting this together.
Repo: github.com/avidevelops/claude-architect-exam-prep
The problem nobody talks about
Most certifications reward memorisation. You grind flashcards, regurgitate definitions, pass, forget everything by Friday.
The CCA-F doesn't work like that. The questions describe real production scenarios and give you four answers that all technically solve the problem. You need to pick the one that reflects best-practice architecture. If your instinct is "just improve the prompt", you'll fail.
There's no official study guide from Anthropic yet. The exam reflects newer patterns in Claude's ecosystem (MCP, subagent orchestration, batch API), so outdated material won't cut it.
Why this changes everything
The GitHub repo (avidevelops/claude-architect-exam-prep) is the closest thing to a structured study plan that exists right now.
33 scenario-based practice questions that mirror the actual exam format. Each one includes a detailed explanation of why the correct answer is correct and why the others fall short.
Covers all three core domains. Agentic architecture, API orchestration, and context management. These make up the bulk of the exam.
Written by someone who actually passed.The tips aren't theoretical. They're patterns the exam taker noticed after sitting the real thing.
The three domains you need to know
Domain 1: agentic architecture
This is about designing multi-agent systems. When to spawn a subagent vs. keeping work in the coordinator. How to decompose workflows. Parallel execution via simultaneous tool calls.
The biggest trap: spawning subagents when the coordinator already holds the context. If the data is already there, processing it locally is the right call. Subagents are for fresh exploration.
Domain 2: API and orchestration
Tool design, structured JSON output for chaining, input constraints. You need to understand:
- Using enums to lock down ambiguous inputs instead of relying on prompt instructions
- Pagination design that prevents context from ballooning
- Machine identifiers over human-readable text (if an agent acts on an item, it needs an explicit ID, not a name)
tool_choiceenforcement for prerequisite dependencies
Domain 3: context management
The exam tests whether you understand information loss in long pipelines. Specific topics:
- The "lost in the middle" effect and how to structure context to avoid it
- Trimming reasoning chains before passing to downstream agents
- Conflict detection flags in structured outputs
- Citation IDs for maintaining provenance across handoffs
5 tips that separate pass from fail
1. Always pick the structural answer
When two options exist and one uses schema constraints, backend enforcement, or explicit IDs while the other says "refine the prompt" or "add instructions", pick the structural one. The exam consistently rewards deterministic solutions over probabilistic ones.
2. Subagent spawning is a trap answer
The repo calls this out specifically. If the coordinator already has the context, don't spawn. Subagents add latency and lose context. Only use them for genuinely independent work streams.
3. MCP annotations are untrusted metadata
Security decisions come from system-level trust boundaries, not what a tool reports about itself. The exam tests whether you understand that MCP server annotations are informational, not authoritative.
4. Batch before you scale
Refine your prompts on representative samples before running large batch jobs. Know how custom_id works for resubmitting only failed documents. Understand SLA math for batch scheduling windows.
5. Budget your context like money
Don't pass full conversation history to downstream agents. Trim raw content. Pass structured findings with metadata. Every token you waste on reasoning chains from upstream is a token your downstream agent can't use for actual work.
How to study
- Read the repo front to back. Start with the overview, then work through all 33 questions. Don't just read the answers. Try to answer each one before looking.
- Focus on the "why". Each question explanation tells you why the wrong answers are wrong. That's where the real learning is. The exam gives you plausible-sounding alternatives.
- Build something with Claude's agent patterns. The exam tests applied knowledge. If you've actually built a multi-agent system, used tool_choice, or managed context in a pipeline, the questions will feel familiar.
- Review the cheat sheet the day before. The repo includes a concepts quick-reference. Good for last-minute review of patterns like tool vs. prompt enforcement and context budgeting strategies.
Honest limitations
This is a community-made resource, not an official Anthropic study guide. The questions are based on one person's experience and may not cover every topic on the exam.
The exam itself is still new. Anthropic could update the question pool at any time, and some domains might shift in weight.
The repo has 170 stars and 40 forks at time of writing. It's well-structured but not battle-tested across thousands of test-takers yet.
Resources
- GitHub repo: github.com/avidevelops/claude-architect-exam-prep
- Reddit post: u/Significant-Hornet37 on r/ClaudeAI
- Anthropic certification page: check anthropic.com for official exam registration
Full credit to u/Significant-Hornet37 for sharing their experience and building the repo.
I'm sitting the exam in about two weeks. If you want updates on how it goes, follow along on Instagram or join the community at nicnonac.com.