Anthropic CCDV-F Prüfungs-Guide, CCDV-F Prüfung

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Anthropic CCDV-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Developer-Foundations CCDV-F Prüfungsfragen mit Lösungen (Q21-Q26):

21. Frage
You are building a Claude application that needs to deliver model output to end users as it is generated, instead of waiting for the full response to complete.
The Claude API mechanism you would use is...

A. Prompt caching, which speeds up the cost profile of future requests and does not affect the delivery timing of the first response.
B. Structured JSON output, which delivers responses only after the model has finalized the JSON shape across the entire response.
C. The Batch API, which delivers full responses after a delay suitable for non-interactive workloads.
D. Streaming responses, which deliver tokens incrementally as the model generates them so users see output progressively.

Antwort: D

Begründung:
Option B is the intended and technically correct answer. The supplied Claude Developer exam source selects streaming responses. Streaming allows the client application to begin receiving output before generation of the complete message has finished, reducing perceived latency for interactive user experiences.
Anthropic's official Messages API documentation states that setting stream: true causes responses to be delivered incrementally through Server-Sent Events (SSE) . Claude can stream text deltas as well as other event types, including structured tool-use and extended-thinking events where applicable. Anthropic's SDKs provide corresponding synchronous or asynchronous streaming helpers.
The application consumes events as they arrive, updates the UI progressively, and then handles the final event or stop reason when generation completes. This architecture is particularly useful for chat interfaces and other latency-sensitive interactive experiences.
A addresses output structure, not progressive transmission. C is designed for asynchronous, throughput- oriented processing rather than immediate user interaction. D reduces processing cost and latency for repeated prompt prefixes but does not itself turn a response into an incremental stream.
Relevant Claude Developer topics: Messages API, streaming, SSE, incremental tokens, latency, event handling, interactive applications, and API response mechanics .

22. Frage
Your Claude application's token costs have grown faster than expected. The team has not been tracking token usage by feature, so the team cannot identify which features are driving cost. The team is debating how to respond.
How would you respond?

A. Switch every feature to the smallest model to cut cost broadly across the application during normal operation.
B. Tell the team that cost growth is unavoidable as the application scales and that no investigation will change the trajectory of the application's cost over the next several quarters.
C. Add token usage tracking by feature to the application's logging so the team can identify which features drive cost before recommending changes.
D. Reduce token usage uniformly across all features by half, applying the cut evenly across the application during normal operation.

Antwort: C

Begründung:
Option D follows the fundamental optimization rule of measuring before changing. Without feature-level usage telemetry, the team cannot know whether cost growth comes from request volume, long prompts, excessive outputs, low cache-hit rates, expensive models, agent loops, tool results, or one particularly inefficient workflow.
Anthropic's Usage and Cost API exists specifically to provide granular historical usage and cost information.
It supports token tracking and breakdowns by dimensions including model, workspace, service tier, API key, context window, and other usage characteristics. Anthropic describes this data as useful for monitoring, cost reconciliation, optimization, and determining whether system changes improve efficiency.
B applies a model downgrade indiscriminately and may damage features whose quality requirements genuinely demand a stronger model. C similarly imposes an arbitrary token reduction without identifying where waste exists. A abandons cost management entirely.
The correct lifecycle is instrument, establish a baseline, identify high-cost features, analyze why they are expensive, apply targeted optimizations, and measure again. Relevant Study Guide topics: observability, usage telemetry, cost attribution, token accounting, production monitoring, capacity planning, and evidence- driven optimization.

23. Frage
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, processing the emails one at a time sequentially to ensure consistent ordering of results.
B. The streaming responses API to process each email and return partial results to a database as the model generates them.
C. The real-time Messages API with concurrent requests to process the emails as fast as possible during the overnight window.
D. The Batch API, which is designed for non-interactive workloads with flexible completion windows.

Antwort: D

Begründung:
Option A is correct because the Message Batches API is designed for asynchronous, high-volume processing where results do not need to be returned interactively. Anthropic's API reference states that a Message Batch can contain many independent Messages requests and may take up to 24 hours to complete. That makes it appropriate for 10,000 overnight email-extraction jobs with a several-hour completion window.
Streaming in B solves a different requirement: it exposes partial response events while a single request is being generated, which is valuable for interactive user experiences or long-running synchronous requests, but it does not provide the workload-management advantages of a batch job. C processes items sequentially and unnecessarily sacrifices throughput. D can increase throughput with concurrent real-time calls, but it adds concurrency management and rate-limit pressure when the workload explicitly tolerates asynchronous completion.
The batch design also lets each request carry a custom identifier so results can be matched back to source emails even if completion order differs. Therefore, A is the intended Claude API choice. Relevant Study Guide topics: Message Batches API, asynchronous processing, high-volume workloads, request correlation, throughput, and non-interactive application design.

24. 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 inside the application's source code as a regular library module and call it from the application code instead of from Claude Code.
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 in a Skills directory recognized by Claude Code, where Claude can discover and load it during sessions for the team's repositories.
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: C

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.

25. Frage
You are designing a Claude application that will process customer support tickets in two stages: a triage stage that classifies tickets and a response stage that drafts replies. The team is debating whether to use a single Claude call that handles both stages or separate Claude calls for each stage.
How would you structure the application?

A. Use a single Claude call for triage and then use a non-Claude rule-based system for response generation, on the grounds that rule-based systems are more reliable for drafting replies.
B. Use separate Claude calls for triage and response, because each stage has distinct inputs, outputs, and success criteria that benefit from focused prompts.
C. Use a single Claude call for both stages, on the grounds that a single call is cheaper than multiple calls in any production Claude application setup.
D. Use multiple Claude calls in parallel that each draft a complete ticket reply, then have a fourth Claude call select the best one to send to the customer.

Antwort: B

Begründung:
Option C follows a prompt-chaining/workflow decomposition pattern. Ticket classification and reply generation are materially different tasks. Triage may require a constrained category, priority, or routing decision, while response generation requires richer context, tone, policy compliance, and customer-facing language. Splitting the stages allows each Claude call to use a prompt, context set, model configuration, output format, and evaluation criterion appropriate to that specific responsibility.
Anthropic's guidance on effective agentic systems distinguishes predictable workflows from open-ended agents and recommends using the simplest architecture that meets requirements. Predefined workflows are particularly appropriate where work can be divided into clear processing stages.
This decomposition also creates useful engineering boundaries: the triage output can be validated before becoming input to the drafting stage; triage accuracy and response quality can be evaluated independently; and either stage can later be optimized without rewriting the entire process.
A unnecessarily restricts the second stage to rules. B introduces expensive parallel generation and a selection step without a stated requirement. D optimizes solely for call count while sacrificing task separation and observability.
The supplied exam source marks C. Relevant topics: Agent Patterns, prompt chaining, workflow decomposition, focused prompts, stage-level evaluation, and modular Claude application design.

26. Frage
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