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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

You are starting a new Claude application and have a small set of well-labeled examples that demonstrate the desired output format. You want to use these examples to guide Claude's behavior.
How would you guide the application's behavior?

A. Use multi-shot prompting by including the labeled examples in the prompt so Claude can match the desired output format on each request.
B. Train a custom model on the labeled examples before deployment so that the application does not need to include the examples in any prompt during operation.
C. Embed the examples in a database and retrieve them at runtime as reference material for the team.
D. Use zero-shot prompting and rely on the model's general capability to produce the desired output format consistently across all incoming requests.

Question 2

You are setting up a Claude application that requires API keys for several external services.
What is the best way to store the keys?

A. Store the keys in a secrets manager or environment-specific configuration that is not checked into source code, and load them at runtime.
B. Email the keys to each developer as needed and have each developer paste the keys into their local environment when they begin working on the application's code.
C. Use a single shared key across all external services, so any developer working on the application can find the keys easily during development.
D. Put the keys in the application's configuration file and check the configuration file into the team's repository alongside the rest of the source code.

Question 3

You are configuring Claude Code for a new project. The team needs to set permissions, default model selections, and environment-specific behavior at the project level so the configuration is consistent across all developers working on the repository.
The Claude Code mechanism you would use is...

A. A shared spreadsheet that lists configuration values for team members to reference and update by hand as the project evolves.
B. Environment variables that each developer sets on their own machine when working with Claude Code on the project.
C. A system prompt embedded in every Claude Code conversation by each developer at the start of every session in the project.
D. The settings.json file, scoped at the project level so the configuration applies consistently across developers and persists with the repository.

Question 4

A team has deployed a multi-agent system in which a primary agent decomposes user requests and delegates subtasks to three specialized subagents: one for data retrieval, one for analysis, and one for report generation.
In production, the team observes that subagents are making redundant tool calls, occasionally exceeding token budgets, and sometimes producing outputs that contradict each other - all of which the primary agent passes along without catching.
What is the most appropriate way to address these failures?

A. Add retry logic to each subagent so that when a tool call fails, the subagent retries automatically before escalating - and configure each subagent to log its tool calls and outputs to a shared trace so the team can audit redundancy and contradictions after the fact.
B. Give each subagent read access to the other subagents' outputs so they can identify and resolve contradictions without routing back through the primary agent.
C. Collapse the three subagents into a single large-context model call that handles retrieval, analysis, and generation in one pass.
D. Strengthen the primary agent's management layer to enforce per-subagent tool budgets, validate outputs against a defined schema before passing them forward, and establish explicit handoff contracts between stages.

Question 5

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

Solutions: