These Anthropic Claude Certified Developer - Foundations (CCDV-F) sample questions give you a first look at how the Claude Developer Foundations exam asks its questions: the question format, the level of detail in each scenario and the kind of reasoning the answer options call for. Each question relates to a syllabus topic, so working through them is a quick way to check how well you understand the material and which topics need more study before the Anthropic Claude Certified Developer - Foundations (CCDV-F) exam.
Free sample questions are a good starting point, but ten questions can only cover a small part of the syllabus. Training, documentation and hands-on work build the knowledge the exam requires. Practice then shows whether you can recall and apply that knowledge without your notes, and checking each answer helps you understand why one option is correct and the others are not. As your preparation moves forward, timed practice helps you keep a steady pace and read questions more carefully under pressure.
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Anthropic CCDV-F Sample Questions:
01. A chat UI feels slow because users see a blank screen for several seconds until Claude's full answer arrives, even though total generation time is acceptable.
Which Claude API capability most directly improves the perceived responsiveness?
a) Streaming the response incrementally as it is generated
b) Vision input, so that the model is also able to process images along with the user's text
c) The Message Batches API, which runs many requests asynchronously at a much lower cost per call
d) Prompt caching, which reuses a stable prefix across the calls to lower the input cost over time
02. In a tool-use loop, Claude returns a response whose stop_reason is tool_use and requests a search_orders call.
What must the application do before the next request so the model can continue correctly?
a) Discard the request and re-send the original prompt so the model is forced to pick a different tool
b) Return the result to the user and end the turn with no further model call at all
c) Run the requested tool and append its result to the conversation before the next request
d) Move the result into the system prompt and begin an entirely fresh conversation from scratch
03. A developer needs an extraction step to always return a structured result by calling a specific tool rather than replying with free text.
Which tool_choice setting enforces that a particular tool is called?
a) The <code>auto</code> setting, which lets the model reply with plain text instead of a tool call
b) A forced choice that names that specific tool, so the model must call exactly it
c) The <code>any</code> setting
d) No setting at all
04. A team wants Claude Code to run inside their CI pipeline and emit machine-parseable review findings that a later job consumes programmatically, with no interactive prompts.
Which approach fits?
a) Run it interactively and have a person copy the findings across into the next job by hand
b) Run it headless but keep its default prose output and grep the text afterward for the issues
c) Have the CI job paste the diff into the desktop app and then read the reply back out
d) Run it headless and request structured JSON output for findings
05. Claude returns a confidently worded answer that cites a specific figure for a downstream financial calculation.
How should the application treat that output?
a) Validate the figure against a trusted source before relying on it
b) Accept it, since a confident tone shows the model is certain it is right
c) Accept it as long as it is formatted as valid JSON, which proves it is accurate
d) Reject every model output for any financial use, since none of it can ever be checked
06. An extraction prompt handles clear cases well but is inconsistent on genuinely ambiguous inputs, sometimes guessing and sometimes refusing.
Which prompting technique most directly improves consistency on the ambiguous cases?
a) Add a single instruction to the system prompt simply telling the model to be consistent
b) Add a few targeted examples that demonstrate how an ambiguous case should be handled and resolved
c) Switch to zero-shot prompting so that no examples can bias the model's decision at all
d) Raise the temperature so that the model explores many more options on the harder cases
07. An extraction service intermittently fails. Logs show the app sent a request whose JSON body was truncated, the API returned a 400, and the code reported it as "model produced bad output." A developer keeps adding retries and few-shot examples with no improvement.
What is the correct first step?
a) Add several more few-shot examples to the prompt so the model stops producing malformed output
b) Increase the retry count further so that the failing request eventually manages to succeed
c) Switch over to a more capable model so that it can handle the difficult input better
d) Attribute the failure to the integration layer and fix the underlying request serialization bug
08. A company must re-categorize three million archived documents with Claude. The job can run overnight, nothing downstream is waiting on any single result, and cost matters.
Which API choice fits best?
a) Synchronous realtime requests sent one after another
b) Streaming requests returning each result token by token
c) The Message Batches API, since the work is bulk and latency-tolerant
d) A multi-agent system fanning out across many subagents
09. A developer authors an MCP server for their team and wants client agents to spend fewer exploratory tool calls discovering what reference material is available, such as issue summaries and schema catalogs.
Which MCP primitive is designed for this?
a) MCP resources, which expose readable content catalogs to the client
b) MCP tools, which are really the only way that a server is able to expose any information to a client at all
c) MCP prompts, whose purpose is to hold the server's own authentication credentials for the connecting client to use
d) The <code>isError</code> flag, which is used to enumerate all of the available server content whenever a call happens to fail
10. After an incident, a team cannot reproduce which exact wording of a system prompt was live when an extraction job began returning malformed output, because the prompt is edited in place in code.
Which practice would make such regressions traceable?
a) Store the prompt only as a string constant that is edited directly in the application source each time
b) Keep prompts in each engineer's own local notes outside the repository
c) Regenerate the prompt from the model on each deploy so it stays current
d) Treat prompts as versioned artifacts so each change is recorded and attributable to a revision
Answers:
Question: 01
Answer: a |
Question: 02
Answer: c |
Question: 03
Answer: b |
Question: 04
Answer: d |
Question: 05
Answer: a |
Question: 06
Answer: b |
Question: 07
Answer: d |
Question: 08
Answer: c |
Question: 09
Answer: a |
Question: 10
Answer: d |
Ready to practice beyond these ten questions? The premium Anthropic CCDV-F practice exam gives you repeated timed attempts across all Claude Developer Foundations syllabus topics, so you can apply what you have studied under exam-like conditions and track your progress from one attempt to the next.
For current exam objectives, registration details and certification updates, see the official Anthropic CCDV-F exam page.
Note: These are practice questions written independently for study and familiarization. They are not official Anthropic exam questions or content recalled from a live exam. If you spot an error in these Anthropic Claude Certified Developer - Foundations (CCDV-F) certification exam sample questions, please email us at feedback@vmexam.com.
