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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

A teammate has asked you to explain when a Skill would be the right choice over an MCP server. The teammate is unsure how the two differ in practice when both can be reused across teams.
How would you explain the distinction?

A. A Skill bundles prompts, scripts, and data into a package the model loads as a unit while an MCP server exposes resources, tools, and prompts through a standard client interface.
B. A Skill and an MCP server are equivalent extension mechanisms that the team can use interchangeably for any reusable capability that needs to be accessible across teams.
C. A Skill is preferable for cross-team reuse because it loads more efficiently than an MCP server during normal operation in the team's typical multi-team workloads.
D. A Skill is the older mechanism and an MCP server is the newer one, so the team should prefer an MCP server for any reusable capability that the team builds going forward.

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

Question 3

Your team is preparing a new Claude application for production, and the product team has asked for a cost projection. The team needs to estimate the cost based on expected request volume, average input length, and average output length. How would you build the projection?

A. Build a cost model that combines expected request volume, average input tokens, average output tokens, the chosen model's pricing, and any caching benefits.
B. Build a cost model based on expected request volume and the chosen model's pricing, treating average input and output token counts as variables to be estimated post-launch.
C. Build a cost model that combines expected request volume and average input tokens, treating output tokens as a small enough share of cost to leave out of the projection.
D. Build a cost model that uses the average per-request cost from a similar Claude application the team built last year, scaling that figure by expected request volume.

Question 4

The team is debating whether to build a new capability as a custom tool or to use an existing built-in tool that nearly covers the use case but lacks one specific feature.
How would you decide?

A. Review the built-in tool's documentation and assess whether its existing capabilities are sufficient to cover the use case without any modification or extension.
B. Identify whether the team has previously built similar custom tools and use those precedents to determine which approach is more consistent with the existing codebase.
C. Examine the custom tool option by scoping the implementation effort and determining whether the additional flexibility justifies the development and maintenance cost.
D. Evaluate whether the missing feature can be addressed by extending the built-in tool or warrants a custom tool, then choose accordingly.

Question 5

Your application uses the Messages API to handle multi-turn conversations. Each new turn resends the entire conversation history, and your token costs are growing as conversations get longer. You suspect there is a more efficient approach.
How would you address this?

A. Switch to the Batch API for every turn so the per-call cost is reduced, treating the batch as the team's primary cost-control mechanism for multi-turn work.
B. Use prompt caching to reuse the static portions of the conversation context across turns instead of paying for them at the normal input-token rate on every request the application sends.
C. Truncate every conversation to the last two messages so that token usage stays low and costs remain predictable across the application's normal operation.
D. Summarize each conversation after every turn and replace the full history with the summary on the next request, reducing token count at the cost of fidelity.

Solutions: