Anthropic CCDV-F : Claude Certified Developer-Foundations

  • Exam Code: CCDV-F
  • Exam Name: Claude Certified Developer-Foundations
  • Updated: Aug 30, 2026   Q&As: 97 Questions and Answers

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Anthropic CCDV-F Exam Syllabus Topics:

SectionWeightObjectives
Model Selection and Optimization16.8%- Model Capabilities and Trade-offs
- Model Selection
- Performance Optimization
- Cost and Latency Optimization
Security and Safety8.1%- Prompt Injection and Untrusted Content
- Safety and Responsible Development
- Application Security
- Secure Tool Use and Guardrails
Eval, Testing, and Debugging2.6%- Evaluation, Testing, and Debugging
Applications and Integration33.1%- Software Engineering Fundamentals
- Multimodal and Structured Outputs
- Streaming, Error Handling and Reliability
- API Integration and Application Development
- Claude API and Client SDKs
- Message Batches and Prompt Caching
Claude Code3.1%- Claude Code Configuration and Extensibility
Agents and Workflows14.7%- Agent Patterns and Frameworks
- Agent Construction with Claude
- Agent Architecture
Tools and MCPs10.6%- Model Context Protocol
- Building Custom Tools and MCP Servers
- Tool Use and Tool Schemas
Prompt and Context Engineering11%- Prompt Engineering
- Context Engineering
- Context Management and Long-Context Techniques

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?

A. Adjust the temperature parameter together with the max_tokens parameter, treating the combined adjustment as the team's mechanism for managing context window pressure during long-transcript processing.
B. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
C. Increase the temperature parameter as the junior developer suggested and observe whether the context window issue resolves over the next several runs of the application in production.
D. Remove the system prompt entirely to make room for longer transcripts in each request, freeing up context window space the system prompt would otherwise consume.


Question 2

Your team's Claude agent has accumulated several customizations that bypass the SDK's defaults, including custom history management, retry logic, and error handling. A new team member has proposed reverting all the customizations to maintain the codebase more easily. The tech lead disagrees and says each customization was added for a reason.
How would you advise the team?

A. Decide on each customization individually based on its original reason and the SDK's current capabilities.
B. Revert all customizations to the SDK's defaults to standardize the codebase.
C. Keep all customizations, trusting that the tech lead's original reasoning is still valid.
D. Migrate the agent off the SDK and rebuild it with a custom loop.


Question 3

A teammate has asked why your Claude application sometimes produces a response that includes the prompt text repeated back, and other times produces a response with the prompt text rephrased. They suspect a bug in the application's request construction.
How would you respond?

A. Explain that LLMs generate output token by token, and variation in how prompt content appears in output is a property of generation, not a bug in request construction.
B. Tell the teammate that the variation depends on which Claude model serves the request, and recommend pinning the application to a single model version to make the output behavior consistent.
C. Confirm that the variation is a bug in the application's request construction and start investigating which part of the application is producing the inconsistent prompt text.
D. Tell the teammate that the variation is caused by the application sending two different prompts on different runs and propose a code change that pins the system prompt to a single version.


Question 4

Your Claude application has multi-step workflows where each step's output is needed only briefly before the agent moves on. The cumulative tool output is filling the context window with content that is no longer relevant.
How would you handle the accumulating tool output?

A. Switch to a smaller Claude model that processes context more efficiently and treat any quality loss as a tradeoff for the cost reduction.
B. Apply tool output pruning to remove tool outputs that are no longer needed by later steps in the workflow.
C. Apply prompt caching to the accumulated tool outputs so the application does not re-pay for the older content on each subsequent step.
D. Keep every tool output in the context indefinitely so the agent has the full record of every step it has executed during the workflow.


Question 5

A Claude application is occasionally refusing to answer questions that should be in scope, including questions the application has answered correctly in the past. You want to investigate.
What is the first step of your investigation?

A. Test whether removing or adjusting those instructions resolves the behavior.
B. Identify which questions have been refused and compare them against previously answered questions to determine what changed between the successful and refused interactions.
C. Examine traces of the refused requests to identify what triggers the refusal, whether input patterns, system prompt content, or other context.
D. Assess whether the refusals cluster around specific input patterns, question types, or time periods by analyzing the distribution of refused requests in the logs.


Solutions:

Question 1
Answer: B
Question 2
Answer: A
Question 3
Answer: A
Question 4
Answer: B
Question 5
Answer: C

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