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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

You are building an MCP server that exposes several internal data sources as MCP resources. The server needs to be deployed so multiple Claude applications can integrate with it.
How would you approach the build and deployment?

A. Build the MCP server with resource and tool definitions scoped to the first Claude application that needs it, and extend the definitions to additional applications as each integration is requested.
B. Author the server with clearly defined resources, tools, and prompts, choose a communication pattern, and deploy to an accessible hosting environment.
C. Deploy the MCP server only on individual developer machines, with the Claude applications unable to reach the server outside each developer's machine.
D. Bypass the MCP server and embed each data source directly in every Claude application that needs the data, with each application maintaining its own integration.

Question 2

Your team is debating how to manage the prompts used in your Claude application. Some prompts are checked into the code repository, some live in a separate configuration file, and some are constructed inline at runtime. The result is inconsistent, and a recent prompt change went out without code review.
What steps would you take?

A. Move all prompts out of version control to a separate spreadsheet that team members can edit freely as the application evolves over time.
B. Establish a single source of truth for prompts, version-control them alongside code, and require code review for prompt changes.
C. Establish a single source of truth for prompts but keep change review optional, allowing developers to update prompts directly when changes are urgent.
D. Move all prompts to inline runtime construction so the team can update them quickly through a streamlined process outside the standard code review workflow.

Question 3

The product team has asked you to choose a Claude model for a new feature. The team has provided functional requirements but has not specified performance, cost, or quality targets. The team's product manager says, "Use whatever model gives us the best results." How would you respond?

A. Run every Claude model on a representative sample and pick whichever scores best on a generic benchmark.
B. Choose the largest, highest-capability Claude model, on the grounds that "best results" is most likely to mean highest quality.
C. Ask the product team to specify quality, latency, and cost targets, then select the model whose tradeoffs best fit those targets.
D. Choose a mid-tier model and ship the feature, because mid-tier models work for most use cases without specified targets.

Question 4

A teammate has asked how to extend Claude Code with a custom Skill that the team can invoke during sessions. The Skill consists of a set of instructions and a few support scripts the team wants Claude to be able to call when the Skill is loaded.
Where is the right place to define the Skill?

A. Define the Skill in a Skills directory recognized by Claude Code, where Claude can discover and load it during sessions for the team's repositories.
B. Define the Skill in a personal scratch directory on each developer's machine and load it manually before each Claude Code session that needs it.
C. Define the Skill inside the application's source code as a regular library module and call it from the application code instead of from Claude Code.
D. Define the Skill as a long inline instruction at the top of every CLAUDE.md file in the team's repositories so Claude has access to it on every session.

Question 5

Your Claude application uses structured output that is consumed by downstream code. The team wants to handle malformed or unexpected output gracefully so it does not crash downstream systems.
The best choice for handling this issue would be to...

A. Silently drop any output that does not match expectations so downstream systems do not see the malformed output the application receives from the model.
B. Apply defensive parsing to handle unexpected fields, missing values, and type mismatches that the downstream code might otherwise crash on.
C. Use the output the model produces and leave schema enforcement to a later phase of production after the application is more complete.
D. Crash on any unexpected output and require manual recovery on affected requests the application handles during normal operation.

Solutions: