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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Applications and Integration	33.1%	- 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 - Claude API and Client SDKs 1. Construct and process API requests and responses 2. Integrate applications with the Claude API and supported client SDKs 3. Implement streaming and handle API errors
Claude Code	3.1%	- Claude Code Configuration and Usage 1. Use CLAUDE.md and project configuration 2. Use Skills, plugins, and Claude Code capabilities effectively 3. Configure settings and development environments
Eval, Testing, and Debugging	2.6%	- Testing and Debugging 1. Test Claude integrations and agentic systems 2. Diagnose and debug application, agent, and integration issues - 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. Apply secure-by-design practices to Claude-powered applications 2. Protect sensitive data and manage access appropriately - Safety and Guardrails 1. Use hooks and other mechanisms to enforce application controls 2. Implement safety controls and guardrails
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
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
Model Selection and Optimization	16.8%	- Model Selection 1. Select appropriate Claude models for task requirements 2. Evaluate quality, latency, capability, and cost tradeoffs - Performance and Cost Optimization 1. Use batching and other approaches to improve efficiency 2. Apply prompt caching and other cost optimization techniques
Agents and Workflows	14.7%	- Claude Agent SDK and Agent Loops 1. Implement and manage custom agent loops 2. Build and configure agents using the Claude Agent SDK - Subagents and Agentic Frameworks 1. Apply appropriate agentic frameworks and orchestration patterns 2. Use subagents and coordinate multi-agent workflows - Agent Architecture and Tradeoffs 1. Evaluate tradeoffs between agentic approaches and traditional workflows 2. Select appropriate agent architectures and patterns

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Anthropic Claude Certified Developer-Foundations CCDV-F Prüfungsfragen mit Lösungen (Q38-Q43):

38. Frage
The team is debating whether to integrate with the Claude API directly or through a third-party abstraction layer that supports multiple LLM providers. The team has identified that all current and projected use cases run on Claude, no internal customer has requested LLM portability, and the team's product roadmap does not mention multi-provider support over the next two years. The third-party abstraction would add roughly 15 percent overhead in code complexity and introduce one additional dependency.
Which integration approach would you recommend?

A. The third-party abstraction layer, on the grounds that multi-provider support is valuable for any application as a matter of long-term flexibility across vendors.
B. Both integration paths in parallel, where the application uses each path on different runs to compare which performs better in production over time.
C. A custom multi-provider abstraction layer the team builds in-house so that the team controls every part of the abstraction the application uses for its API calls.
D. Direct integration with Claude through its SDK, because no multi-provider need exists and abstraction would add complexity that does not pay off.

Antwort: D

Begründung:
D follows the principle of choosing the simplest architecture that satisfies demonstrated requirements .
The supplied examination item explicitly marks D. In this scenario, portability is neither a present functional requirement nor a foreseeable roadmap requirement. Adding an abstraction therefore creates measurable complexity without delivering an identified product capability.
Anthropic provides official general-purpose SDKs for Claude in multiple languages. These expose the Messages API directly while adding idiomatic interfaces, type safety, streaming support, retries, and error handling. Anthropic separately describes compatibility layers and framework-specific libraries as alternative integration surfaces rather than requirements for ordinary Claude development.
A third-party abstraction becomes rational when there is a genuine requirement for multiple providers, vendor switching, standardized cross-model interfaces, or an existing architectural platform that mandates it. None exists here. Building that flexibility speculatively increases dependencies, debugging surface, version- compatibility work, and the possibility that provider-specific Claude capabilities are hidden behind a lowest- common-denominator API.
A optimizes for hypothetical future requirements. B doubles operational complexity. C creates an even larger maintenance burden.
Relevant Claude Developer topics: Claude App Design, SDK selection, abstraction boundaries, dependency management, YAGNI, integration architecture, and provider portability requirements .

39. Frage
Your team uses Claude Code across multiple repositories. You want the team's rules and general coding standards to apply to all repositories, and other rules to apply only to specific repositories. The team is currently duplicating instructions across every repository's CLAUDE.md file.
How would you address this?

A. Stop using CLAUDE.md altogether and ask each developer to configure Claude Code manually for each project they work on.
B. Move all instructions to a separate documentation site that developers consult during Claude Code sessions across all repositories.
C. Use a single repository's CLAUDE.md as the central source of truth and link to it from every other repository's CLAUDE.md file.
D. Use a CLAUDE.md hierarchy that scopes general standards broadly and project-specific context within each repository's local CLAUDE.md.

Antwort: D

Begründung:
B is directly supported by both the supplied examination source and Claude Code's configuration model. The source marks the hierarchical CLAUDE.md approach as correct. Claude Code supports instructions at multiple scopes, allowing broadly applicable standards to be separated from project-specific context rather than duplicated across every repository.
Anthropic documents several CLAUDE.md scopes. Organization-managed instructions can apply broadly; user-level instructions in ~/.claude/CLAUDE.md apply across a user's projects; project instructions in .
/CLAUDE.md or ./.claude/CLAUDE.md provide repository-specific architecture, conventions, commands, and workflows. Claude Code loads applicable files according to the directory hierarchy, allowing broad instructions and more specific local instructions to coexist.
This arrangement improves maintainability because common coding standards are defined once at the appropriate scope, while each repository retains only the context unique to that project. A documentation website does not automatically inject rules into Claude Code context. C creates inconsistent manual configuration. D improperly couples unrelated repositories to one repository's configuration.
Relevant Claude Developer topics: Confia Management, CLAUDE.md hierarchy, organization scope, user scope, project scope, repository configuration, instruction inheritance, and configuration reuse .

40. Frage
You are designing an agent that processes vendor invoices. The work involves a small number of well- understood steps, but occasionally an invoice arrives in an unexpected format that requires the system to decide between rerouting, requesting clarification, or flagging for human review.
The most appropriate architecture for this system is...

A. A fully autonomous agent that handles every invoice from start to finish across all formats.
B. A manager agent that delegates each step of standard invoice processing to a dedicated subagent, with a separate subagent handling each unexpected format.
C. A workflow for the standard path with an agent invoked at the decision point for unexpected formats.
D. A single large prompt that processes every incoming invoice, both standard and unexpected, in one model call.

Antwort: C

Begründung:
Option D applies the correct hybrid pattern. Anthropic distinguishes workflows-where LLMs and tools execute along predefined code paths-from agents, where the model dynamically determines how to proceed.
Anthropic recommends choosing the simplest architecture that meets the requirement: workflows provide predictability and consistency for well-understood tasks, while agents are valuable where flexible model- driven decisions are necessary.
Standard invoice processing is explicitly described as a small number of known steps. That makes a deterministic workflow the appropriate default because the path can be tested, monitored, and reproduced.
The unexpected-format branch is different: the system must reason among rerouting, requesting clarification, or human escalation. That decision point is where agentic flexibility adds value.
A makes the entire process autonomous even though most steps do not require autonomy, increasing cost, latency, and unpredictability. B introduces multiple specialized agents despite no demonstrated need for that complexity. C collapses deterministic processing and exception handling into one monolithic model call, making validation and debugging harder.
Therefore, D combines workflow predictability with targeted agentic reasoning. Relevant Study Guide topics:
workflows versus agents, routing, exception handling, hybrid agentic architecture, escalation, and complexity minimization.

41. Frage
A teammate is debugging a Claude application whose system prompt has grown to several hundred lines and now contains overlapping, contradictory, and obsolete instructions.
How would you advise the teammate?

A. Split the prompt across multiple system prompts so the model sees a smaller portion at any given time.
B. Audit the prompt for overlap, contradiction, and obsolete content, then refactor so each instruction is clear, current, and non-redundant.
C. Add more explicit instructions so the most recent rules dominate the model's interpretation of the prompt during each request.
D. Tighten only the contradictory rules first, treating the overlap and obsolete content as lower-priority work the team can address later.

Antwort: B

Begründung:
Option B is correct. A large prompt containing overlapping, contradictory, or obsolete instructions creates instruction ambiguity and increases the likelihood that Claude will apply an unintended rule, over-weight irrelevant guidance, or produce inconsistent behavior.
Anthropic's current prompting guidance reinforces this principle: Claude performs best when instructions are clear, explicit, and direct. Prompt design should minimize ambiguity and provide the model with precisely the context and behavioral requirements needed for the task. Refactoring should therefore remove obsolete requirements, consolidate duplicated instructions, resolve contradictions, and establish one authoritative formulation for each behavioral rule. The revised prompt should then be regression-tested against representative inputs.
A makes an already overloaded prompt larger and relies on instruction ordering instead of eliminating conflicts. C addresses only one defect while retaining redundant and obsolete material. D does not inherently solve contradictions and may fragment authoritative instructions across multiple locations.
Therefore, B addresses the root cause. Relevant Study Guide topics: system prompt design, prompt maintainability, instruction clarity, conflicting instructions, prompt refactoring, regression evaluation, and context quality.

42. Frage
You are starting a new Claude application and have a small set of well-labeled examples that demonstrate the desired output format. You want to use these examples to guide Claude's behavior.
How would you guide the application's behavior?

A. Train a custom model on the labeled examples before deployment so that the application does not need to include the examples in any prompt during operation.
B. Embed the examples in a database and retrieve them at runtime as reference material for the team.
C. Use multi-shot prompting by including the labeled examples in the prompt so Claude can match the desired output format on each request.
D. Use zero-shot prompting and rely on the model's general capability to produce the desired output format consistently across all incoming requests.

Antwort: C

Begründung:
The supplied examination set identifies A as correct. A small collection of high-quality labeled examples is ideally suited to few-shot or multishot prompting . The examples demonstrate concretely what acceptable input/output behavior looks like, allowing Claude to infer formatting, structure, tone, and task-specific conventions without requiring model retraining.
Anthropic's official prompting guidance states that examples are among the most reliable ways to steer Claude's output format, tone, and structure. It recommends using relevant, diverse examples and clearly separating them from the surrounding instructions. Anthropic currently recommends approximately three to five examples where practical and suggests XML structures such as < examples > and < example > to make prompt organization explicit.
B discards useful supervision by relying exclusively on zero-shot behavior. C is unnecessary for a small fixed set and does not ensure those examples are actually visible to Claude unless additional retrieval logic is created. D introduces unnecessary training complexity for a behavior that prompting already addresses efficiently.
Therefore, A provides the lowest-complexity, highest-leverage solution.
Relevant Claude Developer topics: Agent Construction, multishot prompting, few-shot learning, labeled examples, prompt design, output formatting, behavioral steering, and prompt evaluation .

43. Frage
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