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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Claude Code	3.1%	- Claude Code Configuration and Usage 1. Configure settings and development environments 2. Use Skills, plugins, and Claude Code capabilities effectively 3. Use CLAUDE.md and project configuration
Topic 2: 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
Topic 3: 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. Implement streaming and handle API errors 3. Construct and process API requests and responses
Topic 4: Tools and MCPs	10.6%	- Tool Development and Integration 1. Design and implement custom tools for Claude applications and agents 2. Handle tool schemas, invocation, and tool-use results - Model Context Protocol 1. Apply MCP concepts and patterns for connecting models to external capabilities 2. Build and integrate MCP servers
Topic 5: 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. Use hooks and other mechanisms to enforce application controls 2. Implement safety controls and guardrails
Topic 6: 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
Topic 7: 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. Apply appropriate agentic frameworks and orchestration patterns 2. Use subagents and coordinate multi-agent workflows
Topic 8: 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. Apply prompt caching and other cost optimization techniques 2. Use batching and other approaches to improve efficiency

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Anthropic Claude Certified Developer-Foundations CCDV-F Prüfungsfragen mit Lösungen (Q88-Q93):

88. Frage
You are designing an agent that handles a complex claim-processing workflow. Each claim moves through fact extraction, eligibility evaluation, and a decision step. The three subtasks have distinct success criteria, and some claims require iteration between fact extraction and eligibility evaluation before a decision can be reached.
Which agent pattern would you apply?

A. A graph-based pattern that lets the agent move between subtasks based on the state of each claim, with each subtask evaluated against its own criteria.
B. A linear chain pattern that processes every claim through fact extraction, then eligibility evaluation, then decision, with no return paths between subtasks.
C. A single tool-use loop pattern that gives one agent access to all the tools needed for fact extraction, eligibility evaluation, and decision-making.
D. A streaming pattern that emits partial decisions as the agent processes each claim, refining the output until a final decision emerges from the stream.

Antwort: A

Begründung:
A is correct because the workflow is state-dependent, non-linear, and iterative . The source explicitly identifies the graph-based pattern as the intended architecture for this scenario. Each stage-fact extraction, eligibility evaluation, and decision-making-has its own completion criteria, and the process may need to move backward from eligibility evaluation to fact extraction when information is incomplete. A graph representation naturally models these conditional transitions and loops.
Anthropic's current orchestration guidance supports workflows containing branching, loops, filtering, staged execution, and state-dependent control flow , rather than forcing every task through one fixed sequence.
Dynamic workflow orchestration can use explicit control logic so the next processing stage depends on current state and previous results.
B concerns progressive output delivery, not workflow-state transitions. C provides a generic agentic tool loop but does not explicitly model distinct states or transition criteria. D is unsuitable because it prohibits the required return path between extraction and eligibility evaluation.
Therefore, a graph-based architecture provides the necessary conditional routing, iteration, and stage-specific validation.
Relevant Claude Developer topics: Agent Patterns, graph workflows, state transitions, conditional branching, loops, stage-specific success criteria, and agent orchestration .

89. Frage
Your Claude agent has too many tools, and many of them have overlapping functionality. The agent often picks an inappropriate tool when several could plausibly handle a request.
How would you address the tool selection problem?

A. Restructure the tool set by consolidating overlapping tools, removing unused tools, and clarifying tool descriptions so each tool has a distinct purpose.
B. Add detailed examples to each tool's description so the agent can match incoming requests to the right tool by example, treating the examples as the team's selection mechanism.
C. Remove all tools and rely on the agent's general capability instead, with the application losing the workflows that previously relied on tools.
D. Add more tools to cover every variation of the requests the agent handles, on the grounds that more tools give the agent more accurate options to choose from.

Antwort: A

Begründung:
Option D addresses the architectural cause of the failure: an ambiguous tool surface. Claude chooses tools partly from their names, descriptions, schemas, and the relationship between the request and the capability described. If multiple tools appear to perform substantially the same task, selection becomes unnecessarily difficult.
Anthropic's tool-definition guidance explicitly recommends consolidating related operations into fewer tools and making descriptions clear about both what a tool does and when it should be used. The documentation notes that fewer, more capable tools reduce selection ambiguity and make the available tool surface easier for Claude to navigate.
Option A makes the ambiguity worse by expanding an already overlapping tool set. B destroys required application capabilities. C could improve selection somewhat, because examples can clarify complex inputs, but it leaves the underlying duplication intact. Examples complement good tool design; they are not a substitute for distinct tool responsibilities.
Therefore, D is the strongest solution: remove unused tools, merge functions that represent the same conceptual operation, and write discriminative descriptions defining appropriate and inappropriate usage.
Relevant Study Guide topics: tool design, tool selection, descriptions, tool consolidation, agent construction, and reducing ambiguity.

90. Frage
A teammate has asked you to explain why the team's Claude application is billed for output tokens at a different rate than input tokens. They had assumed the rate was the same for both.
How would you explain the difference?

A. Output tokens are typically billed at a lower rate than input tokens, because output tokens are cheaper to produce than input tokens are to process.
B. Output tokens are typically billed at a higher rate than input tokens, and cost models for the application should reflect both rates separately.
C. Output tokens are not billed at all, because cost is determined entirely by the input tokens sent to the model on each request.
D. Output tokens are typically billed at the same rate as input tokens, and the apparent rate difference is a billing error to report to Anthropic.

Antwort: B

Begründung:
The supplied Claude Certified Developer Foundations question identifies D as the correct answer. Anthropic prices input and output tokens separately, so application cost models must independently account for the number of tokens supplied to Claude and the number generated in response.
Anthropic's official pricing documentation confirms that output-token pricing is normally higher than base input-token pricing. For example, current Claude models maintain distinct columns for base input tokens and output tokens; the exact dollar amounts depend on the selected model. This distinction matters because an application with relatively small prompts but very large generated responses can incur substantial output- token expenditure even when input volume is modest.
Therefore, cost forecasting should use approximately: input tokens × input rate + output tokens × output rate , with additional adjustments where prompt caching, batch processing, server tools, or other pricing modifiers apply. Option A incorrectly assumes equivalent rates, B reverses the normal relationship, and C incorrectly excludes generated tokens from billing.
Relevant Claude Developer topics: Claude API Mechanics, token accounting, model pricing, input/output token usage, cost modeling, prompt caching, and production API economics .

91. Frage
Your application uses the Messages API to handle multi-turn conversations. Each new turn resends the entire conversation history, and your token costs are growing as conversations get longer. You suspect there is a more efficient approach.
How would you address this?

A. Use prompt caching to reuse the static portions of the conversation context across turns instead of paying for them at the normal input-token rate on every request the application sends.
B. Switch to the Batch API for every turn so the per-call cost is reduced, treating the batch as the team's primary cost-control mechanism for multi-turn work.
C. Summarize each conversation after every turn and replace the full history with the summary on the next request, reducing token count at the cost of fidelity.
D. Truncate every conversation to the last two messages so that token usage stays low and costs remain predictable across the application's normal operation.

Antwort: A

Begründung:
Option A is the technically verified answer. Anthropic documents that multi-turn agentic requests resend the growing conversation context on subsequent turns. Prompt caching allows repeated prompt prefixes-such as system instructions, tool definitions, and prior unchanged conversation content-to be reused at the lower cache-read rate rather than repeatedly charged as ordinary uncached input. Anthropic specifically identifies caching repeated context as a major production cost optimization.
B is incorrect even though the supplied PDF contains a duplicated screenshot in which B appears selected.
The Batch API is designed for asynchronous workloads that can tolerate delayed completion; it is not the appropriate mechanism for every interactive turn in a multi-turn conversation. Because the user requested verified answers rather than blindly reproducing marked selections, A is retained.
C discards potentially essential conversation state arbitrarily. D is a valid context-management technique in some long-running workflows, but summarizing after every turn creates additional model work and loses detail; it does not exploit repeated-prefix caching.
Therefore, A directly addresses the stated cost pattern while preserving conversation fidelity. Relevant Study Guide topics: Messages API, stateless conversation history, prompt caching, cached input tokens, multi-turn applications, token economics, and cost optimization.

92. Frage
Your Claude application has multi-step workflows where each step's output is needed only briefly before the agent moves on. The cumulative tool output is filling the context window with content that is no longer relevant.
How would you handle the accumulating tool output?

A. Apply prompt caching to the accumulated tool outputs so the application does not re-pay for the older content on each subsequent step.
B. Apply tool output pruning to remove tool outputs that are no longer needed by later steps in the workflow.
C. Switch to a smaller Claude model that processes context more efficiently and treat any quality loss as a tradeoff for the cost reduction.
D. Keep every tool output in the context indefinitely so the agent has the full record of every step it has executed during the workflow.

Antwort: B

Begründung:
Option A applies the correct context-engineering strategy: remove stale tool results once they no longer contribute useful information to subsequent reasoning. Agentic workflows frequently accumulate search results, file contents, API responses, and intermediate artifacts. Keeping all of them indefinitely consumes the finite context window, raises token cost, and can reduce model focus by introducing low-value information.
Anthropic specifically documents tool result clearing for this situation. Context Editing can remove older tool results when the conversation grows, while preserving recent interactions and optionally retaining tools whose results must remain available. Anthropic describes old tool outputs such as retrieved files or search results as candidates for clearing after Claude has processed them.
Prompt caching in B solves a different problem: it can lower cost and latency for repeated static prompt prefixes, but cached tokens still constitute context and therefore do not eliminate context-window pressure. C changes model capability without solving the architectural cause. D maximizes context pollution.
The correct architecture is therefore to preserve high-signal state while pruning ephemeral intermediate outputs. This aligns with Claude Developer coverage of context engineering, long-running agents, context- window management, tool-result clearing, and efficient agent state management. Anthropic's broader context- engineering guidance likewise emphasizes curating the smallest high-signal context necessary for successful inference.

93. Frage
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