- Exam Code: CCDV-F
- Exam Name: Claude Certified Developer-Foundations
- Updated: Aug 30, 2026
- Q & A: 97 Questions and Answers
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Applications and Integration | 33.1% | - Vision capabilities - Claude Messages API - Streaming and Batch API - SDK and third-party integration |
| Topic 2: Model Selection and Optimization | 16.8% | - Claude model family characteristics - Cost and token optimization - Latency and performance trade-offs |
| Topic 3: Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
| Topic 4: Claude Code | 3.1% | - Claude Code configuration and usage |
| Topic 5: Evaluation, Testing, and Debugging | 2.6% | - Error handling and debugging - Output evaluation and validation |
| Topic 6: Prompt and Context Engineering | 11% | - Prompt design and structuring - Structured output handling - Context window management |
| Topic 7: Agents and Workflows | 14.7% | - Claude Agent SDK usage - Agent architecture principles - Memory and context management - Workflow vs autonomous agents |
| Topic 8: Security and Safety | 8.1% | - AI application security - Guardrails and safety controls |
Question 1
A teammate has asked you to explain the difference between context engineering and prompt engineering.
They have heard the terms used interchangeably and are unsure how each applies to a Claude application that processes long-running multi-step tasks.
How would you describe the distinction?
A. Prompt engineering and context engineering each address content the team gives Claude, but the team can group them under a single workflow because the practices use overlapping techniques.
B. Prompt engineering shapes individual prompts for specific outputs, while context engineering manages how content flows across turns and steps and takes steps to keep relevant state visible.
C. Prompt engineering is the older term for prompt design, while context engineering is the newer term that has replaced it in modern Claude applications across the industry.
D. Prompt engineering focuses on the model's response, while context engineering focuses on the user's input across many sessions in a long-running multi-step Claude application.
Question 2
Your Claude application has been running for several conversation turns, and you notice the model occasionally references information that was discussed many turns ago but is no longer relevant. You suspect context drift is causing the model to weight stale content too heavily.
How would you address the drift?
A. Truncate the conversation so the model sees only the most recent turn during each subsequent response.
B. Reset the conversation after every turn so the model loses all prior turns when generating a response.
C. Apply compaction to summarize older portions of the conversation so the gist remains while the specifics carry less weight.
D. Increase the context window size so all turns of the conversation remain visible to the model in full detail.
Question 3
The Claude application your team built has grown over six months, and the prompt-handling code has accumulated duplication and tangled control flow. The functionality is working, but new features are getting harder to add.
How would you address this?
A. Continue adding features and plan a refactoring pass after the next two release cycles when the team has more bandwidth for internal work.
B. Plan a refactoring pass to consolidate duplicated logic, separate concerns, and simplify control flow before adding new features.
C. Move all the prompt-handling code into a single large function to reduce the number of files developers have to navigate when reading the code.
D. Refactor the prompt-handling code in small increments as part of each new feature ticket, treating the cleanup as a side effect of feature work.
Question 4
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. 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.
B. 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.
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. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
Question 5
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 system prompt embedded in every Claude Code conversation by each developer at the start of every session in the project.
B. The settings.json file, scoped at the project level so the configuration applies consistently across developers and persists with the repository.
C. A shared spreadsheet that lists configuration values for team members to reference and update by hand as the project evolves.
D. Environment variables that each developer sets on their own machine when working with Claude Code on the project.
Solutions:
| Question 1 Answer: B | Question 2 Answer: C | Question 3 Answer: B | Question 4 Answer: D | Question 5 Answer: B |
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