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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question #1
A Claude application is producing outputs that drift away from the expected JSON format after several conversation turns. The first few turns produce correctly formatted output, but later turns gradually lose structure.
How would you address the drift?

A. Truncate every response to the first few characters, validate that the truncated output matches the expected JSON structure, and log any mismatches for review.
B. Switch to a smaller Claude model and re-test the application to determine whether the drift persists across conversation turns.
C. Restart the application after every turn and monitor whether the format remains consistent across subsequent interactions.
D. Identify the failure mode as format drift, examine how the conversation context evolves over turns, and address the drift through context management or output validation.

Question #2
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 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.
B. 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.
C. 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.
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 #3
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. Move all prompts to inline runtime construction so the team can update them quickly through a streamlined process outside the standard code review workflow.
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. Establish a single source of truth for prompts, version-control them alongside code, and require code review for prompt changes.

Question #4
Your Claude application makes high-volume API calls during business hours and very few calls overnight.
The team is concerned about staying within rate limits during peak hours and wants to understand how the Claude API enforces those limits.
How would you proceed?

A. Examine the peak-hour request patterns in your application logs and smooth traffic by distributing requests more evenly across the business-hours window.
B. Assess the average payload size of current API calls and consolidate requests where possible to reduce the total number of calls made during peak hours.
C. Identify the rate limits, design the application to stay within them during peak hours, and use exponential backoff when limits are reached.
D. Review the API documentation for streaming endpoints and evaluate whether migrating peak-hour calls to streaming reduces exposure to rate limit enforcement.

Question #5
Your Claude application runs long agentic workflows where the agent makes many tool calls, and the conversation history grows quickly. After about 20 tool calls, you notice the agent's responses become less focused and sometimes ignore earlier task constraints.
How would you address this?

A. Apply context engineering techniques such as tool output pruning or compaction to keep the active task state visible while reducing the volume of older content.
B. Restart the agent every five tool calls to prevent any drift, with the agent losing all task state at each restart point during the workflow.
C. Remove tool calling from the workflow entirely so the agent operates as a single text-generation step with no tool outputs accumulating in the context window.
D. Increase the model's context window so the agent can hold every tool output at full detail across the entire workflow no matter how many tool calls it accumulates.

Solutions: