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Anthropic CCDV-F Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Selection and Optimization | 16.8% | - Cost and Latency Optimization - Model Capabilities and Trade-offs - Model Selection - Performance Optimization |
| Topic 2: Prompt and Context Engineering | 11% | - Context Management and Long-Context Techniques - Prompt Engineering - Context Engineering |
| Topic 3: Security and Safety | 8.1% | - Safety and Responsible Development - Secure Tool Use and Guardrails - Application Security - Prompt Injection and Untrusted Content |
| Topic 4: Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Topic 5: Agents and Workflows | 14.7% | - Agent Architecture - Agent Patterns and Frameworks - Agent Construction with Claude |
| Topic 6: Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Topic 7: Tools and MCPs | 10.6% | - Tool Use and Tool Schemas - Model Context Protocol - Building Custom Tools and MCP Servers |
| Topic 8: Applications and Integration | 33.1% | - API Integration and Application Development - Message Batches and Prompt Caching - Multimodal and Structured Outputs - Streaming, Error Handling and Reliability - Claude API and Client SDKs - Software Engineering Fundamentals |
Anthropic Claude Certified Developer-Foundations Sample Questions:
You are extending a Claude agent with a capability that needs to be reusable across multiple teams in the organization, with each team able to invoke and use it independently.
How would you build the capability?
- A. As a custom tool embedded in this team's agent only, with other teams able to copy the implementation into their own agents when they need the capability.
- B. As a shared library that each team imports into its own Claude application code, with each team responsible for keeping the library up to date in its integration.
- C. As a Skill or MCP server because both are purpose-built for cross-team reuse independently by each consuming team.
- D. As a wrapper around an existing built-in tool that adds the missing functionality, on the grounds that built-in tools cover the reuse pattern when extended carefully.
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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 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.
- B. 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.
- 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 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.
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You are designing a Claude application that will require structured JSON output for downstream processing.
The output schema is well-defined, and downstream systems will reject malformed JSON.
- A. Define a clear schema and structure the prompt to request output in that schema, with downstream systems handling any validation needed.
- B. Structure the prompt to request output in a schema that is described in plain English, with downstream systems parsing whatever shape Claude produces.
- C. Avoid structured output and use free-form text everywhere instead, on the grounds that free-form text is more flexible and handles edge cases better than structured schemas.
- D. Define a clear schema, structure the prompt to request output in that schema, and validate Claude's output against the schema before passing it downstream.
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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. 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.
- B. 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.
- C. 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.
- D. 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.
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You are establishing the guardrail strategy for a Claude application. The team wants to ensure guardrail failure does not expose the application to unsafe behavior.
The guardrail strategy would...
- A. Layer multiple guardrails so a single guardrail failure does not expose the application to unsafe behavior.
- B. Apply guardrails at the model level only and rely on the model's built-in safety behaviors to handle any cases the guardrail does not explicitly cover.
- C. Implement a single comprehensive system prompt guardrail and validate its coverage against the application's full range of expected inputs.
- D. Apply guardrails at the application output level only and route flagged responses to a human reviewer before they are delivered to the user.
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