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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Tools and MCPs	10.6%	- Tool Development and Integration 1. Handle tool schemas, invocation, and tool-use results 2. Design and implement custom tools for Claude applications and agents - Model Context Protocol 1. Build and integrate MCP servers 2. Apply MCP concepts and patterns for connecting models to external capabilities
Topic 2: Security and Safety	8.1%	- Secure Application Design 1. Apply secure-by-design practices to Claude-powered applications 2. Protect sensitive data and manage access appropriately - Safety and Guardrails 1. Implement safety controls and guardrails 2. Use hooks and other mechanisms to enforce application controls
Topic 3: Model Selection and Optimization	16.8%	- Performance and Cost Optimization 1. Apply prompt caching and other cost optimization techniques 2. Use batching and other approaches to improve efficiency - Model Selection 1. Select appropriate Claude models for task requirements 2. Evaluate quality, latency, capability, and cost tradeoffs
Topic 4: Eval, Testing, and Debugging	2.6%	- Testing and Debugging 1. Diagnose and debug application, agent, and integration issues 2. Test Claude integrations and agentic systems - Evaluation 1. Interpret evaluation results and improve application quality 2. Design and run evaluations for Claude-powered applications
Topic 5: Claude Code	3.1%	- Claude Code Configuration and Usage 1. Use Skills, plugins, and Claude Code capabilities effectively 2. Configure settings and development environments 3. Use CLAUDE.md and project configuration
Topic 6: Prompt and Context Engineering	11%	- Context Engineering 1. Apply context management strategies for agents and long-running workflows 2. Manage context windows and application context - Prompt Engineering 1. Apply prompting techniques to improve reliability and output quality 2. Design prompts appropriate to application requirements
Topic 7: Agents and Workflows	14.7%	- Subagents and Agentic Frameworks 1. Use subagents and coordinate multi-agent workflows 2. Apply appropriate agentic frameworks and orchestration patterns - Agent Architecture and Tradeoffs 1. Evaluate tradeoffs between agentic approaches and traditional workflows 2. Select appropriate agent architectures and patterns - Claude Agent SDK and Agent Loops 1. Build and configure agents using the Claude Agent SDK 2. Implement and manage custom agent loops
Topic 8: Applications and Integration	33.1%	- Application Development and Integration 1. Handle multimodal and structured application inputs and outputs 2. Integrate Claude capabilities into existing software systems and workflows 3. Build and ship production-grade Claude-powered applications - Claude API and Client SDKs 1. Integrate applications with the Claude API and supported client SDKs 2. Construct and process API requests and responses 3. Implement streaming and handle API errors

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q27-Q32):

NEW QUESTION # 27
Your Claude application receives untrusted input from external sources. The team is establishing how the application should treat this untrusted input.
Untrusted input would be...

A. Routed through a separate Claude application with more lax security controls so the original application does not handle the untrusted input directly.
B. Validated and sanitized before being incorporated into prompts, and treated as data the model should not interpret as instructions.
C. Blocked entirely so the application accepts only trusted input, with no path for legitimate external input the application might process.
D. Treated identically to trusted input from internal sources, on the grounds that all input the application receives can be handled the same way.

Answer: B

Explanation:
Option A is the appropriate trust-boundary treatment for external content. Untrusted text can contain malformed data, adversarial instructions, prompt-injection attempts, or content deliberately constructed to alter the agent's behavior. It should therefore be validated or screened before inclusion and clearly represented as untrusted data rather than authoritative application instructions.
Anthropic's prompt-injection guidance distinguishes direct attacks from indirect prompt injection, where Claude processes third-party content such as webpages, emails, documents, or tool output containing hostile instructions. Anthropic recommends input validation and screening, least-privilege access, safe treatment of untrusted tool content, and screening content before Claude acts on it. Importantly, Anthropic notes that tool- result content is treated as untrusted data rather than as a reliable place for application instructions.
B erases the trust distinction and exposes the application to instruction/data confusion. C introduces a deliberately weaker security boundary and does not sanitize the data. D prevents legitimate use cases unnecessarily; untrusted does not mean unusable-it means the data must be handled defensively.
Therefore, A correctly combines validation with explicit trust separation. Relevant Study Guide topics:
prompt injection, untrusted content, input validation, sanitization, data/instruction separation, least privilege, and defense in depth.

NEW QUESTION # 28
Your team is preparing a new Claude application for production, and the product team has asked for a cost projection. The team needs to estimate the cost based on expected request volume, average input length, and average output length. How would you build the projection?

A. Build a cost model that combines expected request volume and average input tokens, treating output tokens as a small enough share of cost to leave out of the projection.
B. Build a cost model that uses the average per-request cost from a similar Claude application the team built last year, scaling that figure by expected request volume.
C. Build a cost model based on expected request volume and the chosen model's pricing, treating average input and output token counts as variables to be estimated post-launch.
D. Build a cost model that combines expected request volume, average input tokens, average output tokens, the chosen model's pricing, and any caching benefits.

Answer: D

Explanation:
Option B is correct because Claude API cost is fundamentally driven by usage volume multiplied by the token economics of the selected model and features. A credible projection must therefore estimate requests, average input tokens per request, average output tokens per request, applicable input/output prices, and expected prompt-cache behavior. Anthropic's pricing documentation treats input, output, cache writes, and cache reads as distinct billable categories, with cache hits priced below standard input processing.
A practical forecast can be modeled as: request volume × expected per-request input cost plus request volume
× expected per-request output cost, adjusted for cache-write/read rates and any other applicable pricing modifiers. The model should also include ranges for variance rather than only one point estimate, because token lengths and cache-hit rates will fluctuate in production.
Option A assumes a historical application has the same token profile and pricing, which may be false. Option C omits output tokens even though output pricing can be a material share of cost. Option D postpones the very token estimates the product team needs before launch.
Therefore, B is the complete projection method. Relevant Study Guide topics: token accounting, model pricing, prompt caching, cost forecasting, workload sizing, and production economics.

NEW QUESTION # 29
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. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
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. 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.

Answer: A

NEW QUESTION # 30
You are explaining to a stakeholder why running the same Claude prompt twice can produce slightly different results. The stakeholder is concerned this means the application is broken.
How would you address the stakeholder's concern?

A. Tell the stakeholder the variation is a bug that the team will fix in the next release of the application, then create a work ticket to fix the bug.
B. Tell the stakeholder the variation is caused by Claude being updated continuously by Anthropic, and that switching to a fixed model snapshot will eliminate the variation entirely.
C. Tell the stakeholder the variation comes from network latency and that switching to a faster network connection will produce more consistent results across runs.
D. Explain that LLMs are non-deterministic by default due to sampling, and describe how the application handles this through validation, retries, or temperature adjustment.

Answer: D

Explanation:
Option C correctly explains a fundamental property of generative language models. The supplied examination material identifies C as the correct answer. Claude generates subsequent tokens probabilistically rather than retrieving a single fixed answer for each prompt. Consequently, identical or highly similar requests can produce variations in wording, ordering, explanation depth, and sometimes substantive details.
Anthropic's API documentation explicitly states that sampling parameters control randomness and, importantly, that even configurations historically using a temperature of 0.0 were not fully deterministic .
Current newer Claude models increasingly manage sampling behavior internally, so application design should not assume byte-for-byte identical responses across executions.
Production systems therefore handle variation through architecture: schema validation for machine-consumed output, deterministic business-rule checks, retries where appropriate, evaluations for acceptable behavioral ranges, and application-level safeguards. Temperature adjustment may reduce variation on models that support the parameter, but it does not fundamentally convert an LLM into a deterministic function.
A incorrectly labels normal model behavior as a defect. B confuses network latency with generation variability. D overstates model snapshot behavior; pinning a model prevents silent model-version changes but does not eliminate sampling variability.
Relevant topics: sampling, nondeterminism, validation, retries, model versions, and robust Claude API integration .

NEW QUESTION # 31
A teammate has asked you to explain why your Claude agent's tools include detailed descriptions in the tool definition, even when the tool name is already descriptive. The teammate suggests removing the descriptions to simplify the tool definitions.
How would you respond?

A. Agree with the teammate because tool names are sufficient for the model to choose the right tool on every request the agent handles.
B. Suggest moving the descriptions out of the tool definition and into a separate documentation file the team maintains so the tool definitions stay short and the descriptions remain available.
C. Explain that the model uses the tool description to decide when to call the tool, and descriptions disambiguate cases where the tool name is not enough.
D. Suggest replacing the descriptions with example calls embedded in the tool definition, treating example calls as a complete substitute for the prose description.

Answer: C

Explanation:
The supplied exam source identifies C . Tool descriptions are part of Claude's runtime decision context. A descriptive function name can indicate what a tool appears to do, but it may not clearly tell Claude when to use it, when not to use it, what assumptions apply, or how it differs from similar tools .
Anthropic's official tool-use documentation states that Claude determines whether to call a tool based on the user's request and the tool's description. Its tool-definition guidance defines description as detailed plaintext describing what the tool does, when it should be used, and how it behaves. Anthropic's troubleshooting guidance also identifies ambiguous descriptions as a cause of selecting the wrong tool and recommends differentiating tools by when they should be used, not only by what they do.
A can supplement descriptions with examples but should not universally replace them. B incorrectly assumes names provide sufficient semantics. D removes the information from Claude's actual decision context.
Therefore, tool descriptions are operational metadata for model routing, not merely documentation for developers.
Relevant Claude Developer topics: Agent Construction, tool definitions, tool descriptions, tool selection, schema design, disambiguation, routing, and reliable tool use .

NEW QUESTION # 32
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