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Anthropic CCDV-F Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Security and Safety | 8.1% | - Safety and Guardrails
|
| Agents and Workflows | 14.7% | - Subagents and Agentic Frameworks
|
| Claude Code | 3.1% | - Claude Code Configuration and Usage
|
| Model Selection and Optimization | 16.8% | - Performance and Cost Optimization
|
| Tools and MCPs | 10.6% | - Model Context Protocol
|
| Prompt and Context Engineering | 11% | - Context Engineering
|
| Eval, Testing, and Debugging | 2.6% | - Evaluation
|
| Applications and Integration | 33.1% | - Claude API and Client SDKs
|
Anthropic Claude Certified Developer-Foundations Sample Questions:
Question 1
A teammate is reviewing the team's threat model for a Claude application and has asked you to identify the categories of AI-specific threats that the model should cover. The teammate has already listed traditional web application threats and wants to know what additional categories apply to a Claude application.
Which AI-specific threat categories would you add?
A. Network-level denial of service and physical infrastructure attacks, because these categories cover the threats most likely to affect any Claude application in production.
B. Supply chain attacks on the Claude SDK because the SDK itself is the only point of vulnerability that a Claude application introduces beyond traditional web application threats.
C. Cross-site scripting and SQL injection, because these traditional web application threats apply with equal weight to any application that uses Claude in any way.
D. Prompt injection, data leakage from prompts or context, jailbreak attempts, and unsafe model output that bypasses application controls.
Question 2
You are deciding between deploying a Claude-powered agent on Anthropic's hosted infrastructure or self- hosting under a "bring your own cloud" model in your own AWS account. The agent processes customer data subject to your enterprise's data residency policies, but the team wants to ship quickly and avoid managing infrastructure.
Which deployment model would you recommend?
A. Deploying on Anthropic-hosted infrastructure while the team drafts a request to update the enterprise data residency policy to accommodate hosted AI deployments.
B. Deploying on Anthropic-hosted infrastructure to meet the team's shipping timeline, and flagging the data residency requirement for a follow-up compliance review after launch.
C. Self-hosting under BYOC to satisfy the data residency requirement, while working with the infrastructure team to reduce the operational overhead of managing the deployment.
D. Self-hosting under BYOC for an initial pilot, then evaluating whether to migrate to Anthropic-hosted infrastructure once the agent's data-handling patterns are better understood.
Question 3
Your Claude application returns confident-sounding answers, but occasionally those answers contain factual errors that downstream systems treat as ground truth. The team is concerned about the application's confidence-versus-accuracy gap.
How would you address the gap?
A. Add a disclaimer to every output telling users to verify the accuracy of the output and treat the disclaimer as the primary mechanism for managing the confidence-versus-accuracy gap.
B. Lower the model's temperature so the model's responses sound less confident and downstream systems are less likely to treat the responses as ground truth in normal operation.
C. Reject every response the application produces until a manual accuracy review is conducted on each response by a human reviewer before any downstream system uses it.
D. Apply skepticism toward confident output by adding validation steps, sourcing requirements, or confidence calibration before treating outputs as ground truth.
Question 4
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 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.
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 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.
Question 5
You are building a Claude application that processes 10,000 customer emails overnight to extract structured data. The work is non-interactive, runs once daily, and has a flexible completion window of several hours.
Which Claude API would you use?
A. The real-time Messages API with concurrent requests to process the emails as fast as possible during the overnight window.
B. The Batch API, which is designed for non-interactive workloads with flexible completion windows.
C. The streaming responses API to process each email and return partial results to a database as the model generates them.
D. The real-time Messages API, processing the emails one at a time sequentially to ensure consistent ordering of results.
Solutions:
| Question 1 Answer: D | Question 2 Answer: C | Question 3 Answer: D | Question 4 Answer: C | Question 5 Answer: B |



