CCDV-F Pass Dumps & PassGuide CCDV-F Prüfung & CCDV-F Guide

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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Model Selection and Optimization	16.8%	- Latency and performance trade-offs - Claude model family characteristics - Cost and token optimization
Applications and Integration	33.1%	- Streaming and Batch API - Claude Messages API - Vision capabilities - SDK and third-party integration
Tools and Model Context Protocol (MCP)	10.6%	- MCP server development - Tool integration and usage
Security and Safety	8.1%	- Guardrails and safety controls - AI application security
Agents and Workflows	14.7%	- Claude Agent SDK usage - Workflow vs autonomous agents - Memory and context management - Agent architecture principles
Claude Code	3.1%	- Claude Code configuration and usage
Prompt and Context Engineering	11%	- Prompt design and structuring - Structured output handling - Context window management
Evaluation, Testing, and Debugging	2.6%	- Error handling and debugging - Output evaluation and validation

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Anthropic Claude Certified Developer-Foundations CCDV-F Prüfungsfragen mit Lösungen (Q11-Q16):

11. Frage
A Claude application that worked well in testing is now occasionally returning outputs that mention information not present in the input. The development team initially assumed the model was hallucinating, so they asked you to troubleshoot.
What would you do first?

A. Replace the current model with a larger one to reduce the chance of hallucination, on the grounds that larger models tend to hallucinate less in typical applications.
B. Apply a retrieval-augmented generation pattern to ground the responses in source content before any further investigation of the production traces.
C. Examine production traces to identify whether the issue is hallucination by the model, context loss, prompt injection, or another failure mode before recommending a fix.
D. Add a system prompt instruction telling the model not to invent information, on the grounds that prompt-level instructions are the fastest fix for hallucination concerns.

Antwort: C

Begründung:
Option A follows disciplined production debugging: diagnose the actual failure mode before changing architecture or prompts. An output containing unsupported information might indeed be hallucination, but similar symptoms can result from stale conversation state, incorrect retrieval, unexpected tool output, prompt injection, incorrect request construction, or mismatched model/configuration versions.
A production trace should capture the user input, system instructions, relevant conversation history, retrieved content, tool calls and results, model/version, request parameters, response, and identifiers necessary to compare successful and failing cases. This establishes whether the model invented a fact or whether that fact entered context through another path.
B changes the model before establishing causality. C may eventually be useful if the confirmed problem is insufficient grounding, but implementing RAG before diagnosis can hide rather than explain the defect. D similarly changes prompting before verifying that prompt behavior is responsible.
The engineering sequence should be observe, reproduce, classify the failure, form a hypothesis, apply a targeted correction, and validate the correction with evaluations. Relevant Study Guide topics: production troubleshooting, observability, tracing, hallucination analysis, prompt injection, context failures, regression diagnosis, and lifecycle monitoring.

12. Frage
A teammate has asked how to extend Claude Code with a custom Skill that the team can invoke during sessions. The Skill consists of a set of instructions and a few support scripts the team wants Claude to be able to call when the Skill is loaded.
Where is the right place to define the Skill?

A. Define the Skill in a Skills directory recognized by Claude Code, where Claude can discover and load it during sessions for the team's repositories.
B. Define the Skill as a long inline instruction at the top of every CLAUDE.md file in the team's repositories so Claude has access to it on every session.
C. Define the Skill inside the application's source code as a regular library module and call it from the application code instead of from Claude Code.
D. Define the Skill in a personal scratch directory on each developer's machine and load it manually before each Claude Code session that needs it.

Antwort: A

Begründung:
Option C matches Claude Code's documented Skill architecture. Agent Skills are filesystem-based extension artifacts rather than ordinary application modules or repeated prompt fragments. A Skill is represented by a directory containing a required SKILL.md file and can include optional supporting scripts, templates, examples, and reference material.
Anthropic documents project Skills under .claude/skills/ < skill-name > /SKILL.md. Project-level Skills can be shared through Git and automatically discovered when Claude Code loads project settings. Supporting scripts can reside alongside the Skill and be referenced from SKILL.md.
A incorrectly embeds reusable procedural material into every CLAUDE.md file, creating duplication and loading instructions even when they are irrelevant. B creates a conventional source-code library but does not register a Claude Code Skill. D makes the capability dependent on undocumented, developer-specific setup and undermines team reuse.
Therefore, C uses the extension mechanism specifically designed for discoverable, reusable Claude capabilities. Relevant Study Guide topics: Agent Skills, .claude/skills, SKILL.md, supporting resources, filesystem discovery, project-level configuration, and reusable Claude Code capabilities.

13. Frage
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, structure the prompt to request output in that schema, and validate Claude's output against the schema before passing it downstream.
B. Define a clear schema and structure the prompt to request output in that schema, with downstream systems handling any validation needed.
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. Structure the prompt to request output in a schema that is described in plain English, with downstream systems parsing whatever shape Claude produces.

Antwort: A

Begründung:
Option C establishes the strongest application boundary between probabilistic model generation and deterministic downstream processing. When another component requires JSON with a known contract, the application should explicitly define the expected structure and ensure that model output conforms to it before downstream execution. Anthropic's current Structured Outputs guidance states that structured outputs constrain responses to a specific schema and are intended to provide valid, parseable data for downstream processing. The current Claude API supports JSON Schema through output_config.format, while SDK helpers can additionally parse and validate returned data.
The underlying engineering principle remains the same even when structured-output enforcement is unavailable: never allow unvalidated model-generated structures to become trusted machine input. Option A provides insufficient contractual control. Option B defines the schema but pushes validation too late, increasing the probability that malformed or semantically invalid data reaches dependent components. Option D sacrifices machine reliability entirely.
Therefore, C correctly combines schema specification, explicit format guidance, and validation. This corresponds to Claude Developer topics covering structured outputs, defensive application design, schema validation, and reliable model-to-system interfaces. The question and options are reproduced from the supplied examination set.

14. Frage
You are setting up Claude Code for a new project repository. Your team has shared coding standards, preferred libraries, and project-specific context that every developer working on the repository should have available when they use Claude Code.
How would you set this up?

A. Initialize Claude Code in the repository and document the standards and project-specific context in a CLAUDE.md file at the repository root.
B. Configure Claude Code through environment variables that each developer sets on their own machine when they begin working in the repository.
C. Document the standards in a separate wiki page maintained outside the repository so the documentation stays decoupled from the source code.
D. Add the coding standards and project context to the repository's existing README file and direct developers to reference it when starting Claude Code sessions.

Antwort: A

Begründung:
Option C is correct. Claude Code uses CLAUDE.md as the standard mechanism for supplying persistent project-level instructions and context. Anthropic's Claude Code documentation states that project instructions can be stored in ./CLAUDE.md or ./.claude/CLAUDE.md and shared with team members through source control. Appropriate content includes coding standards, architectural decisions, project conventions, build/test commands, preferred workflows, and information developers would otherwise need to repeat to Claude during every session. The /init command can also generate an initial CLAUDE.md based on the repository.
A wiki may be valuable for human documentation, but Claude Code does not automatically receive that material as project context. B creates developer-specific configuration and risks inconsistency across the team. D places the information in a human-facing README but does not use Claude Code's purpose-built persistent instruction mechanism.
Therefore, C gives the repository a shared, version-controlled source of Claude-specific project guidance.
Relevant Study Guide topics: CLAUDE.md, project configuration, persistent instructions, repository context, team standards, and configuration management.

15. Frage
You are reviewing an architectural diagram for a Claude-powered travel-booking system. The diagram shows a top-level component that interprets user requests and three subordinate components that handle flights, hotels, and ground transportation. The top-level component is responsible for routing each request, sequencing the subordinate components, and reconciling their outputs into a final itinerary. The diagram also shows that each subordinate component has its own tool list and own short conversation history that is not shared with the others.
Which architectural pattern does this diagram most closely describe?

A. A retrieval-augmented pattern, where the top-level component retrieves relevant context from the subordinate components' indexed data stores before generating each response.
B. A manager and supervisor pattern with isolated context per subagent, where the top-level agent coordinates specialized subagents that each maintain their own conversation history and tool list.
C. A pipeline pattern, where each component processes the user request in sequence and passes the full conversation history along with the request to the next component in the pipeline.
D. A manager and supervisor pattern with shared context, where the top-level agent and the subagents all share a single conversation history that grows as the request flows through the system.

Antwort: B

Begründung:
The architecture is a manager/supervisor-or orchestrator/subagent-pattern with isolated subagent context.
The defining characteristics are a coordinating top-level agent, specialized subordinate agents, delegated responsibilities, and distinct tool/context boundaries. The supervisor determines which specialist should act, sequences work where necessary, and integrates the specialists' outputs into the final result.
Anthropic's published multi-agent research architecture uses this same structural principle. Its Research system employs an orchestrator-worker pattern in which a lead agent analyzes the task, creates specialized subagents, delegates separate research responsibilities, and consolidates their findings. Anthropic emphasizes that good delegation requires each subagent to receive a specific objective, task boundaries, expected output, and appropriate tools.
The question additionally specifies that each travel specialist has its own conversation history and tool list.
That eliminates B because context is explicitly not shared globally. C would involve fixed sequential processing rather than supervisor-directed specialization. D describes retrieval augmentation, not autonomous subordinate agents.
Therefore, A precisely matches both the hierarchy and context-isolation characteristics described.
The supplied examination set also marks A as the intended answer. Relevant topics: Agent Patterns, manager
/supervisor architecture, orchestrator-worker systems, subagents, delegation, specialized tools, and context isolation.

16. Frage
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