Free sample exam
Claude Certified Architect – Foundations
A free sample of the questions on Anthropic's certification for people who build with Claude, scored as you go, with the reason for every answer.
Tecyfy is independent of Anthropic. These are our own practice questions, written to the topics in the official exam guide.
The real exam
The exam tests whether you can choose the most reliable, proportionate design for a production system built with Claude: agent loops, tools and MCP, Claude Code, prompts and structured output, and keeping long tasks reliable. Every question sits inside a scenario.
- Questions
- 60, multiple choice and multiple response
- Time
- 120 minutes
- Pass mark
- 720 on a scale of 100 to 1,000
- Scenarios
- 4 of 6 per sitting, chosen at random
- Fee
- $125
- Valid for
- 12 months
What it covers, by share of the questions
- Agentic architecture and orchestration27%
- Tool design and MCP integration18%
- Claude Code configuration and workflows20%
- Prompt engineering and structured output20%
- Context management and reliability15%
The sample
10 questions in the style of the exam. Pick an answer to see why it is right or wrong. Your best score stays in this browser.
Question 1 of 10
Question 1
Developer productivity. Your agent's own loop ends a run as soon as a response contains text. Engineers say it often stops right after writing "Let me check the imports next", without checking anything. What should decide whether the loop goes on?
Not this time. One response can hold text and tool calls together, so text is no sign the work is over.
stop_reasonsays it:tool_usemeans Claude wants a tool run and the result sent back,end_turnmeans it has finished. A cap is a safety net, not the way a loop should end. Searching the reply for words is the parsing of natural language the guide names as an anti-pattern, and an extra call to ask costs more and can be wrong too.Question 2
Developer productivity. An agent session spent an hour mapping a legacy billing module. You want to try two refactoring approaches, each starting from that analysis, without one attempt's reasoning leaking into the other. What do you do?
Not this time. Forking gives you a second session that starts from the same history, and the original stays as it was, so both attempts share the hour of analysis and nothing else. Asking one session to set an attempt aside doesn't take it out of its context. Mapping the module twice throws the work away. Starting fresh from a summary is the right call when earlier tool results have gone stale; here they still hold, and a summary loses detail. One thing to know: a fork copies the conversation, not the files, so edits made in either fork land in the same folder.
Question 3
Multi-agent research. Every question goes through all four subagents (search, document analysis, synthesis and report), so "What year was the company founded?" takes six minutes and comes back as a long report. What should change?
Not this time. Deciding which subagents a question needs is the coordinator's job. A one-fact question may need one search and a short answer, not the whole pipeline. Running all four at once still runs all four, and synthesis can't start before there is something to combine. A larger model plans the same fixed route. Stored reports only answer what was asked before, and they go out of date.
Question 4
Customer support. When the order database times out,
lookup_orderreturns an empty list, the same as for a customer with no orders. The agent has told several customers they have never ordered anything. What should the tool return on a timeout?Not this time. An empty list is a real answer: the search worked and found nothing. A timeout is a search that failed, and the agent has to know that to retry, wait or tell the customer the truth. MCP reports a tool failure in the result with
isErrorset, and a category and a retry flag let the agent pick the next step. A line in the description can't say which call failed. A bare "Operation failed" hides whether a retry could help. Retrying without end leaves the customer waiting: a short retry inside the tool is fine, but what it can't fix has to come back as an error.Question 5
Code generation with Claude Code. The team uses Jira and wants every developer's Claude Code to reach it, without anyone's API token going into the repository. What do you set up?
Not this time.
.mcp.jsonin the project is the shared, version-controlled place for servers the whole team uses, and Claude Code expands${VAR}references in it, so the file can be committed while each developer keeps the token in their own environment. For a common tool like Jira, a ready-made community server is a better choice than building your own. A token in code is a secret in the repository.~/.claude.jsonis for personal servers, and copies set up by hand drift apart. An ignored.mcp.jsonis no longer shared.Question 6
Code generation with Claude Code. Before a change, Claude has to find every use of a deprecated date helper across a large repository. Last time, the search output filled so much of the context that the later edits lost track of the plan. What helps most?
Not this time. The Explore subagent is made for noisy searching: it's read-only, does its searching apart from your conversation and returns a summary, so the main conversation keeps room for the plan and the edits.
/clearthrows away the findings with the noise. Reading every file first fills the context even faster than the search did. Plan mode holds back edits until you approve a plan; it doesn't keep search output out of the context.Question 7
Claude Code in CI. Your review job runs again on every new commit to a pull request, and each run posts its findings again, so one pull request collects the same comment five times. What should the job do?
Not this time. The review doesn't know what it said last time unless you tell it. Given the earlier findings and asked for only issues that are new or still not fixed, it stops repeating itself and still catches problems pushed later. Matching text word for word fails as soon as a run words the same issue differently. Reviewing only the first run misses everything after it.
--max-turnslimits the turns within one run; it knows nothing about earlier runs.Question 8
Structured data extraction. A check validates each extracted contract and retries with the error when it fails. For some contracts every retry fails on
hourly_rate: the contract says "rates as set out in Schedule B", and Schedule B is a separate file you don't send. What do you do?Not this time. Retrying with the error works when the model got the format or the structure wrong. It can't work when the fact isn't in what the model was given: no number of tries finds a rate that sits in another document. Send that document, or let the field be empty and pass the case to a person. More retries pay for the same failure again. The error in the system prompt changes nothing, because the rate still isn't there. A stricter schema pushes the model to make up a number, which is worse than an empty field.
Question 9
Claude Code in CI. One session writes the code for a pull request and then reviews it. Its reviews nearly always pass, and the bugs turn up later. What gives the review the best chance of catching them?
Not this time. The session that wrote the code still holds the reasoning behind it, so it tends to agree with its own choices. A separate instance with only the diff and the review criteria has no such pull; Anthropic's Claude Code guidance says the same, that a fresh context improves review because Claude isn't biased toward code it just wrote. An instruction to be critical and extended thinking both keep the same context. A second review in the same session repeats the same blind spots, and keeping only issues found twice drops real ones.
Question 10
Multi-agent research. Two credible sources give different figures for the share of small firms using AI: 34% in a 2024 survey and 41% in a 2025 one. The synthesis agent has to put this in the report. What should it do?
Not this time. Two credible figures that differ are part of the finding. Keeping each with its source and date lets the reader see what each one measured, and the dates show this may be a change over a year rather than a contradiction. An average is a number neither source reported. Quietly keeping only the newer figure hides a source the reader may need, and dropping the figure leaves a gap the report never mentions.
Sources
Where each fact comes from
This page was last checked against these sources on 14 October 2026.
- 1Claude Certified Architect – Foundations Exam Guide, version 1.0 (July 2026)
Anthropic · 14 October 2026
The exam has 60 multiple-choice and multiple-response items in 120 minutes, drawn from 4 of 6 scenarios; the pass mark is a scaled 720 on 100 to 1,000; the fee is $125 and the credential is valid for 12 months. Domain weights are 27, 18, 20, 20 and 15%. The task statements behind every question, including stop_reason loop control, fork_session, coordinators that pick subagents by query, isError and structured error metadata, access failures versus empty results, project .mcp.json with environment variable expansion and community servers, the Explore subagent, re-reviews that report only new issues, the limits of retry, independent review instances, and annotating conflicting figures with sources and dates.
- 2Handling stop reasons
Anthropic, Claude API documentation · 14 October 2026
A stop_reason of tool_use means Claude is calling a tool, and the tool should be run and its result returned; end_turn means the response is finished.
- 3Work with sessions
Anthropic, Claude Agent SDK documentation · 14 October 2026
Resuming with fork_session gives a new session with a copy of the history and leaves the original unchanged. Forking branches the conversation history, not the filesystem.
- 4Create custom subagents
Anthropic, Claude Code documentation · 14 October 2026
Explore is a fast, read-only agent for searching and analysing codebases, with Write and Edit denied, and it keeps exploration results out of the main conversation's context.
- 5Connect Claude Code to tools via MCP
Anthropic, Claude Code documentation · 14 October 2026
Project-scope servers are shared with everyone in the project through the .mcp.json file, and Claude Code expands environment variables in .mcp.json so teams can share a configuration while keeping values such as API keys out of it.
- 6Tools, MCP specification 2026-07-28
Model Context Protocol · 14 October 2026
Tool execution errors are reported in the tool result with isError set to true, and carry feedback a language model can use to correct itself and retry.
- 7Best practices for Claude Code
Anthropic, Claude Code documentation · 14 October 2026
A fresh context improves code review, because Claude won't be biased toward code it just wrote.