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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Claude Code	3.1%	- Claude Code Configuration and Usage 1. Use CLAUDE.md and project configuration 2. Configure settings and development environments 3. Use Skills, plugins, and Claude Code capabilities effectively
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
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. Design and run evaluations for Claude-powered applications 2. Interpret evaluation results and improve application quality
Security and Safety	8.1%	- Secure Application Design 1. Protect sensitive data and manage access appropriately 2. Apply secure-by-design practices to Claude-powered applications - Safety and Guardrails 1. Implement safety controls and guardrails 2. Use hooks and other mechanisms to enforce application controls
Tools and MCPs	10.6%	- Model Context Protocol 1. Apply MCP concepts and patterns for connecting models to external capabilities 2. Build and integrate MCP servers - Tool Development and Integration 1. Handle tool schemas, invocation, and tool-use results 2. Design and implement custom tools for Claude applications and agents
Prompt and Context Engineering	11%	- Prompt Engineering 1. Apply prompting techniques to improve reliability and output quality 2. Design prompts appropriate to application requirements - Context Engineering 1. Manage context windows and application context 2. Apply context management strategies for agents and long-running workflows
Model Selection and Optimization	16.8%	- Performance and Cost Optimization 1. Use batching and other approaches to improve efficiency 2. Apply prompt caching and other cost optimization techniques - Model Selection 1. Evaluate quality, latency, capability, and cost tradeoffs 2. Select appropriate Claude models for task requirements
Applications and Integration	33.1%	- Application Development and Integration 1. Integrate Claude capabilities into existing software systems and workflows 2. Build and ship production-grade Claude-powered applications 3. Handle multimodal and structured application inputs and outputs - Claude API and Client SDKs 1. Implement streaming and handle API errors 2. Integrate applications with the Claude API and supported client SDKs 3. Construct and process API requests and responses

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Anthropic Claude Certified Developer-Foundations CCDV-F Prüfungsfragen mit Lösungen (Q14-Q19):

14. Frage
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. 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.
B. Continue adding features and plan a refactoring pass after the next two release cycles when the team has more bandwidth for internal work.
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. Plan a refactoring pass to consolidate duplicated logic, separate concerns, and simplify control flow before adding new features.

Antwort: D

Begründung:
The supplied Claude Developer item selects A . The symptoms-duplicated logic, tangled control flow, and increasing difficulty adding features-indicate accumulated technical debt in a component that has become structurally difficult to change. Because the problem is already materially reducing development velocity, deliberate refactoring should occur before additional feature pressure compounds it.
The objective is behavioral preservation with structural improvement. Duplicated prompt construction should be consolidated where the behavior is genuinely shared. Prompt preparation, configuration, API invocation, validation, error handling, and post-processing should have clear responsibilities. Control flow should be simplified so each stage is testable independently. Existing tests should first capture important current behavior so refactoring can proceed without silently changing application semantics.
B can be appropriate for minor cleanup, but the question describes broad systemic complexity. Mixing substantial structural refactoring into unrelated feature tickets makes scope, review, and regression analysis harder. C knowingly allows the debt to compound. D centralizes complexity rather than removing it and violates separation of concerns.
Relevant Claude Developer topics: SW Eng Foundations, refactoring, technical debt, DRY, separation of concerns, maintainability, modular design, regression testing, and control-flow simplification .

15. Frage
Your Claude agent performs database operations. A recent incident occurred where the agent ran a destructive query that affected production data. The team wants to add deterministic controls to prevent similar incidents.
How would you prevent similar incidents?

A. Switch to a higher-capability Claude model on the grounds that a more capable model is less likely to run destructive queries during normal operation across all requests.
B. Add Claude hooks that intercept database operations and apply deterministic checks, such as blocking destructive queries or requiring approval, before the queries execute.
C. Add a system prompt instruction telling the agent to be careful with database operations on every request the application handles during normal operation across all incoming traffic.
D. Run the agent only during business hours when humans are available to monitor its activity, treating the schedule as the primary control mechanism for destructive operations.

Antwort: B

Begründung:
Option B is correct because destructive production operations require deterministic enforcement outside the model's probabilistic reasoning. Claude Code hooks can intercept lifecycle events before tool execution and explicitly allow, deny, or request further handling based on concrete rules.
Anthropic's hooks documentation provides this exact security pattern. A PreToolUse hook can inspect a proposed command before execution and return a blocking decision. Anthropic's example demonstrates blocking destructive operations such as drop table, while other commands proceed normally.
That mechanism can be adapted to database controls: block DROP, destructive DELETE, unauthorized schema modifications, or production writes; require explicit approval for high-risk operations; and allow read- only or known-safe queries automatically.
A merely increases the probability that someone might notice an unsafe operation and does not prevent execution. C assumes model capability can replace access controls, which is an unacceptable safety boundary.
D is useful behavioral guidance but remains probabilistic and cannot guarantee prevention.
Therefore, B creates a deterministic control between model intent and side-effect execution. Relevant Study Guide topics: Claude hooks, PreToolUse, tool governance, deterministic enforcement, approval gates, least privilege, and destructive-operation protection.

16. Frage
Your Claude application's prompt was written months ago and has not been updated. The team has discovered through evals that the prompt produces good results on common cases but underperforms on a specific category of inputs that has grown in volume.
How would you respond?

A. Replace the prompt with a new one aligned to the underperforming category, treating any common-case performance change as a known tradeoff.
B. Add the underperforming category to a separate Claude application with its own prompt so the original prompt does not change.
C. Tell users to avoid the underperforming category by adding warnings in the application's user interface about handled inputs.
D. Iterate on the prompt to address the underperforming category, validate the change with evals, and continue refining as needed.

Antwort: D

Begründung:
The correct response is iterative prompt improvement backed by evaluation , making A the appropriate choice. The supplied Claude Certified Developer Foundations material explicitly selects A. When production input distribution changes, a prompt that previously met requirements can become inadequate. The correct engineering response is not to preserve the prompt merely because it once worked; prompts are application components that should evolve with observed workload behavior.
Anthropic's official evaluation guidance describes prompt development as a cycle involving test cases, an initial prompt, iterative testing and refinement, final validation, and deployment. Success criteria should be specific and measurable, and evaluations should include representative cases that expose known failure modes.
The underperforming category should therefore be incorporated into the evaluation set. The team can modify instructions, examples, context organization, or other prompt components, then compare the revised prompt against both the newly important category and existing common cases. This prevents improvement in one segment from silently producing regressions elsewhere.
B avoids the defect instead of correcting it. C risks degrading previously successful behavior. D creates unnecessary architectural fragmentation.
Relevant Claude Developer topics: prompt iteration, evaluation-driven development, regression testing, representative test sets, prompt optimization, and production feedback loops .

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

Antwort: C

Begründung:
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.

18. Frage
You are setting up a Claude application that will run a mix of multi-turn conversations and one-off requests.
You want to use caching techniques to reduce token costs where they apply. A teammate suggests caching the model's output as well, so the application does not have to make duplicate Claude calls when similar queries arrive.
You would apply prompt caching to...

A. The static portions of prompts that are repeated across requests, such as system prompts, instructions, or shared context.
B. Nothing, because prompt caching does not affect cost in any application that mixes multi-turn conversations and one-off requests in a single deployment.
C. Only the user's input portion of each request because user input is the part of the prompt that varies the most across the application's normal operation.
D. The model's output, treating the response from each request as cacheable content the application can return on similar future queries.

Antwort: A

Begründung:
The supplied exam source marks D . Claude prompt caching is designed for repeated prompt prefixes, not semantic caching of generated answers. High-value cache candidates include stable system prompts, long instructions, tool definitions, shared background documents, repeated examples, and the previously accumulated prefix of a multi-turn conversation.
Anthropic explains that prompt caching reuses a matching prompt prefix and can substantially reduce processing time and input-token cost on subsequent requests. The cache operates across the request structure- tools, system content, and messages up to the relevant cache boundary. It is particularly useful for prompts with many examples, large repeated context, repetitive instructions, and long multi-turn conversations.
B describes response caching , which is a separate application-level technique and is not what Anthropic's prompt caching feature does. C targets the portion that usually changes most, making it a poor general cache boundary. A is incorrect because multi-turn workloads are a major prompt-caching use case.
Therefore, maximize reusable stable prefixes and place changing request-specific content after them where practical.
Relevant Claude Developer topics: Claude API Mechanics, prompt caching, cache prefixes, token-cost optimization, static context, system prompts, multi-turn conversations, and API efficiency .

19. Frage
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