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2026 Statistics

Key Facts: Claude Certified Architect - Foundations Exam

720/1000

Passing Score

Anthropic Claude Partner Network

Exam Questions

Anthropic (multiple-choice)

120 min

Exam Time

Anthropic

Exam Domains

Anthropic certification page

$125

Exam Fee

Anthropic certification page

Mar 2026

Launch

Anthropic first official certification

CCAR-F requires a scaled score of 720 out of 1000 across 60 multiple-choice and multiple-response questions in 120 minutes. The exam costs $125, is valid for 12 months, and is delivered online-proctored or at a Pearson VUE test center.

Sample Claude Certified Architect - Foundations Practice Questions

Try these sample questions to test your Claude Certified Architect - Foundations exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1A team is building a customer-support agent. They want the simplest reliable architecture and only plan to add complexity if needed. According to Anthropic's guidance on building effective agents, what should they start with?

A.A multi-agent swarm where dozens of agents negotiate the answer

B.The simplest solution possible, such as a single LLM call with retrieval and in-context examples, adding agentic steps only when simpler approaches fall short

C.An orchestrator-worker system with at least five specialized workers from day one

D.A fine-tuned model dedicated to support before writing any prompts

Explanation: Anthropic's 'Building Effective Agents' guidance is explicit: find the simplest solution possible and only increase complexity when needed. Many use cases are solved by a single optimized LLM call with retrieval and examples, and full agentic systems should be reserved for when simpler patterns demonstrably fall short. This keeps systems transparent, cheaper, and easier to evaluate.

2An architect needs to classify incoming requests and send each to a specialized prompt: billing questions to one prompt, technical issues to another, and general questions to a third. Which workflow pattern best fits this requirement?

A.Parallelization

B.Evaluator-optimizer

C.Prompt chaining

D.Routing

Explanation: Routing classifies an input and directs it to a specialized downstream prompt or model. It works well when there are distinct categories better handled separately and when classification can be done accurately. Separating concerns this way lets you optimize each path independently without one prompt trying to handle everything.

3A coding task requires generating a draft, then critiquing it against quality criteria, then refining it, repeating until the critique passes. Which pattern describes this, and what is the key design principle?

A.Orchestrator-worker, with one orchestrator spawning all critics at once

B.Routing, where a classifier picks the best draft from several

C.Evaluator-optimizer, separating generation and evaluation into distinct LLM calls with an adversarial evaluator stance

D.Parallelization, generating many drafts and averaging them

Explanation: The evaluator-optimizer pattern uses one call to generate and another to evaluate and give feedback, looping until criteria are met. Anthropic recommends separating generation from evaluation into distinct calls and giving the evaluator an adversarial stance so it finds real problems. This is one of the highest-leverage improvements for quality-sensitive tasks.

4A research agent must handle tasks where the number and nature of subtasks cannot be predicted in advance: a central model must dynamically break the work down, delegate pieces, and synthesize the results. Which pattern is the best fit?

A.Prompt chaining with a fixed sequence of steps

B.Orchestrator-worker

C.Sectioning a single prompt into headers

D.Routing to one of three static categories

Explanation: In the orchestrator-worker pattern, a central LLM dynamically decomposes a task, delegates subtasks to worker LLMs, and synthesizes their results. It is specifically suited to problems where you cannot predict the subtasks ahead of time, unlike fixed chains or static routing. The orchestrator adapts the plan based on what it discovers.

5An architect wants to distinguish a 'workflow' from an 'agent' in Anthropic's terminology. Which statement is correct?

A.Workflows let the LLM dynamically direct its own process and tool usage; agents follow predefined code paths

B.Workflows and agents are identical terms with no practical difference

C.Agents never use tools, while workflows always do

D.Workflows orchestrate LLMs and tools through predefined code paths; agents dynamically direct their own processes and tool usage

Explanation: Anthropic defines workflows as systems where LLMs and tools are orchestrated through predefined code paths, while agents are systems where the LLM dynamically directs its own processes and tool usage, controlling how it accomplishes the task. The distinction guides architecture choices: workflows offer predictability, agents offer flexibility for open-ended problems.

6A latency-sensitive content pipeline must run a translation, a sentiment analysis, and a toxicity check on the same input, all independent of each other, then combine the results. Which pattern minimizes end-to-end latency?

A.Prompt chaining the three checks sequentially

B.Parallelization (sectioning), running the three independent calls concurrently and aggregating

C.Evaluator-optimizer looping over each check

D.A single giant prompt asked to do all three plus self-grade

Explanation: Parallelization runs independent subtasks simultaneously and aggregates their outputs. When subtasks do not depend on one another, sectioning them into concurrent calls reduces wall-clock latency compared to sequential chaining. It can also improve focus, since each call handles one concern.

7An agent autonomously calls tools in a loop. Anthropic recommends giving the agent guardrails and a way to stop. Which design element most directly bounds runaway loops in a production agent?

A.Setting a maximum number of turns/iterations and clear stopping conditions

B.Increasing max_tokens to the model's maximum on every call

C.Disabling all tool use after the first call

D.Removing the system prompt to reduce token cost

Explanation: Autonomous agents run in a loop, so you must bound them with stopping conditions and a maximum number of turns to prevent runaway cost or infinite loops. The Agent SDK exposes options like maxTurns for exactly this. Clear stop criteria are a core reliability guardrail for agentic systems.

8When designing a multi-step agent, Anthropic emphasizes 'showing the agent's planning steps' and keeping the design simple. What is the primary architectural benefit of this transparency?

A.It guarantees the agent never makes a mistake

B.It eliminates the need for any evaluation or testing

C.It makes the agent's behavior easier to debug, audit, and trust, and easier to course-correct

D.It allows the model to skip tool calls entirely

Explanation: Anthropic recommends prioritizing transparency by explicitly showing the agent's planning steps. Exposing the plan makes behavior observable, so engineers can debug, audit, and intervene when the agent goes off track. Transparency is a core operability principle for production agents, not a correctness guarantee.

9A team uses the Claude Agent SDK and spawns several subagents in parallel for a large refactor. Each subagent does deep analysis. What is the main architectural advantage of using separate subagents here?

A.Subagents share one context window, so all analysis is visible to every agent

B.Subagents automatically inherit all parent permissions, removing any approval prompts

C.Subagents cannot use tools, which makes them faster

D.Each subagent gets its own context window, so one subagent's exploration does not pollute the others' or the main context

Explanation: In the Claude Agent SDK, each subagent runs with its own isolated context window, so verbose exploration in one subagent does not consume or pollute the main conversation or sibling subagents. This context preservation is a primary reason to use subagents for parallelizable, context-heavy subtasks. The main agent can then synthesize concise results.

10An architect must choose a model for a high-volume, latency-sensitive classification step inside a larger agent, where most decisions are simple. Which model selection strategy aligns with Anthropic's guidance?

A.Always use the largest Opus model everywhere for maximum quality

B.Use a fine-tuned model because base models cannot classify

C.Use a faster, cheaper model such as Haiku for the simple high-volume step, reserving larger models for genuinely hard reasoning steps

D.Route every step to a random model to balance load

Explanation: Anthropic's model lineup is tiered: Haiku for fast, low-cost, high-volume tasks; Sonnet for balanced general work; Opus for the hardest reasoning. A good architecture matches each step to the cheapest model that meets the quality bar, using Haiku for simple high-volume classification and reserving Opus for complex reasoning. This optimizes both cost and latency.

About the Claude Certified Architect - Foundations Exam

Claude Certified Architect - Foundations (current Pearson code CCAR-F; formerly CCA-F) is Anthropic's architecture certification for designing, deploying, and maintaining production-grade Claude applications. The exam covers agentic architecture and orchestration (27%), tool design and MCP integration (18%), Claude Code configuration and workflows (20%), prompt engineering and structured output (20%), and context management and reliability (15%).

Questions

60 scored questions

Time Limit

120 minutes

Passing Score

720/1000

Exam Fee

$125 (Anthropic)

Claude Certified Architect - Foundations Exam Content Outline

27%

Agentic Architecture & Orchestration

Design agent and multi-agent systems with the Claude Agent SDK: agent loops, subagents, orchestrator-worker and evaluator-optimizer patterns, prompt chaining, routing, parallelization, and selecting Opus, Sonnet, or Haiku for each role

18%

Tool Design & MCP Integration

Design tool-use schemas, integrate Model Context Protocol servers, apply tools/resources/prompts, choose transports, handle errors, and secure tool and data access

20%

Claude Code Configuration & Workflows

Configure Claude Code for production: CLAUDE.md project memory, settings.json, permissions and modes, slash commands, hooks, subagents, headless and CI usage, and wiring MCP servers into developer workflows

20%

Prompt Engineering & Structured Output

Write system prompts with clear role and task framing, use examples and extended thinking, define structured outputs and tool-based JSON schemas, set stop sequences, and reliably parse model responses in production

15%

Context Management & Reliability

Manage the context window, use prompt caching, implement retries and timeouts, apply RAG and retrieval, build evals and guardrails, and optimize latency, cost, and reliability under failure conditions

How to Pass the Claude Certified Architect - Foundations Exam

What You Need to Know

Passing score: 720/1000
Exam length: 60 questions
Time limit: 120 minutes
Exam fee: $125

Keys to Passing

Complete 500+ practice questions
Score 80%+ consistently before scheduling
Focus on highest-weighted sections
Use our AI tutor for tough concepts

Claude Certified Architect - Foundations Study Tips from Top Performers

1Prioritize the highest-weighted domain (27%): agentic architecture and orchestration with the Claude Agent SDK, subagents, and multi-agent patterns

2Know when to apply each agent pattern: prompt chaining, routing, parallelization, orchestrator-worker, and evaluator-optimizer — and the trade-offs of each

3Understand model selection: when to use Opus vs Sonnet vs Haiku based on capability, latency, and cost for a given agent role

4Master MCP primitives cold: tools, resources, and prompts, plus stdio vs HTTP transports and how MCP servers are configured in Claude Code

5Practice structured output: tool-based JSON schemas, stop sequences, and reliable parsing — the exam tests production-grade output handling, not casual prompting

6Study Claude Code configuration: CLAUDE.md project memory, settings.json, permissions and modes, hooks, and slash commands

7Know reliability levers: prompt caching, retries and timeouts, context-window management, RAG, evals, and guardrails for production failure handling

8Complete 100+ practice questions across all five domains and review every wrong answer until you can explain the architecture principle behind the correct choice

Frequently Asked Questions

What is the CCAR-F passing score?

CCAR-F requires a scaled score of 720 on a 100-1000 range. The current exam has 60 multiple-choice and multiple-response questions in 120 minutes.

How much does the CCAR-F exam cost?

The current CCAR-F exam fee is $125 USD. Each retake is charged at the full exam fee.

What does the CCAR-F exam cover?

CCAR-F covers agentic architecture (27%), tool design and MCP (18%), Claude Code (20%), prompt engineering and structured output (20%), and context management and reliability (15%).

What prior experience do I need for CCAR-F?

Anthropic lists no formal prerequisite. The exam guide targets solution architects with roughly six or more months of practical Claude experience.

How long should I study for CCAR-F?

Most candidates need 4-12 weeks (roughly 40-100 hours) depending on prior hands-on experience. Spend significant time building: configure MCP servers, design multi-agent workflows with the Claude Agent SDK, and practice structured-output and reliability patterns. Anthropic Academy offers free aligned courses including Building with the Claude API, Claude Code 101, and Introduction to MCP.

What is MCP and why does it matter for CCAR-F?

Model Context Protocol connects Claude to external tools, resources, and prompts. Tool Design and MCP Integration is 18% of the official blueprint.

How is CCAR-F delivered and when can I retake it?

The exam is online-proctored or delivered at a Pearson VUE test center. Failed attempts require 14-, 30-, then 90-day waits, with four attempts per rolling 12 months.