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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Prompt and Context Engineering	11%	- Context Management and Long-Context Techniques - Prompt Engineering - Context Engineering
Tools and MCPs	10.6%	- Model Context Protocol - Tool Use and Tool Schemas - Building Custom Tools and MCP Servers
Claude Code	3.1%	- Claude Code Configuration and Extensibility
Agents and Workflows	14.7%	- Agent Patterns and Frameworks - Agent Architecture - Agent Construction with Claude
Security and Safety	8.1%	- Safety and Responsible Development - Secure Tool Use and Guardrails - Prompt Injection and Untrusted Content - Application Security
Applications and Integration	33.1%	- Claude API and Client SDKs - Streaming, Error Handling and Reliability - Multimodal and Structured Outputs - Software Engineering Fundamentals - Message Batches and Prompt Caching - API Integration and Application Development
Eval, Testing, and Debugging	2.6%	- Evaluation, Testing, and Debugging
Model Selection and Optimization	16.8%	- Cost and Latency Optimization - Model Capabilities and Trade-offs - Model Selection - Performance Optimization

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최신 Claude Certified Developer CCDV-F 무료샘플문제 (Q23-Q28):

질문 # 23
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. Direct integration with Claude through its SDK, because no multi-provider need exists and abstraction would add complexity that does not pay off.
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. 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.

정답：A

설명：
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 .

질문 # 24
The product team has asked you to choose a Claude model for a new feature. The team has provided functional requirements but has not specified performance, cost, or quality targets. The team's product manager says, "Use whatever model gives us the best results." How would you respond?

A. Choose the largest, highest-capability Claude model, on the grounds that "best results" is most likely to mean highest quality.
B. Choose a mid-tier model and ship the feature, because mid-tier models work for most use cases without specified targets.
C. Run every Claude model on a representative sample and pick whichever scores best on a generic benchmark.
D. Ask the product team to specify quality, latency, and cost targets, then select the model whose tradeoffs best fit those targets.

정답：D

설명：
Option A is correct because model selection requires measurable non-functional requirements, not an undefined objective such as "best." Anthropic's model-selection guidance explicitly frames the decision around capabilities, speed, and cost and recommends identifying those requirements before narrowing the candidate models.
"Quality" must also be translated into task-specific success criteria-for example classification accuracy, extraction correctness, acceptable false-positive rate, or human preference. Latency may be constrained by an interactive SLA, and cost may be defined per request, per user, or per completed task. Once those thresholds exist, representative evaluations can compare candidate models using the actual prompts and data.
B uses a generic benchmark that may not correlate with the application's workload. C chooses a model tier without requirements or evidence. D optimizes only for raw capability and ignores latency and cost, potentially producing an unnecessarily expensive architecture.
Thus, requirements definition precedes technology selection. Relevant Study Guide topics: Understanding Reqs, functional versus non-functional requirements, quality thresholds, latency budgets, cost constraints, acceptance criteria, workload-specific evaluation, and model-selection tradeoffs.

질문 # 25
You are writing a system prompt for a Claude application that needs to produce output in a specific JSON shape. The downstream system will reject any output that does not match the schema.
Your prompt would need to...

A. Omit any reference to the format and rely on a post-processing step in the application to reshape Claude's output.
B. Instruct Claude to return JSON sometimes and free text other times so the application's output captures both formats.
C. Include explicit constraints describing the required JSON schema and an instruction to produce only output matching that schema.
D. Instruct Claude to use whichever output format it considers most appropriate for each request the application handles.

정답：C

설명：
The supplied question selects D . If a downstream component requires an exact machine-readable structure, the expected structure must be communicated explicitly rather than left to Claude's discretion. The prompt should define required fields, types, nesting, permissible values where relevant, and instruct Claude not to emit surrounding prose.
Anthropic's consistency guidance states that developers should precisely specify the desired output format when format consistency matters. More importantly, current Claude APIs provide Structured Outputs for cases requiring guaranteed JSON Schema conformance; Anthropic explicitly recommends Structured Outputs instead of prompt-only techniques when valid schema-compliant JSON is mandatory.
Therefore, D is the strongest prompt choice among the listed alternatives. In a contemporary production implementation, the design can be strengthened further by supplying the schema through Claude's structured- output configuration and performing downstream semantic validation where business rules exceed JSON Schema.
A permits arbitrary formatting. B intentionally creates inconsistent representations. C assumes post- processing can reliably reconstruct missing or ambiguously formatted information, which is significantly less robust than specifying the contract up front.
Relevant Claude Developer topics: system prompts, JSON formatting, structured outputs, schema constraints, output contracts, validation, and downstream integration reliability .

질문 # 26
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. 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.
B. 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.
C. Apply a retrieval-augmented generation pattern to ground the responses in source content before any further investigation of the production traces.
D. 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.

정답：A

설명：
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.

질문 # 27
Your Claude application produces good responses for typical inputs but struggles with edge cases. You have several labeled examples of edge-case inputs and the desired response for each. You want to use these examples to improve the model's handling of edge cases.
What is the best way to use these examples?

A. Embed the examples in a database for the model to find during inference.
B. Tell users to avoid submitting the edge-case inputs to the application by adding warnings in the application's user interface.
C. Train a custom model on the edge-case examples and deploy that custom model in place of the team's current Claude integration.
D. Add the labeled edge-case examples to the prompt as few-shot examples so the model can learn the pattern.

정답：D

설명：
Option B applies few-shot, or multishot, prompting, one of Anthropic's recommended techniques for steering Claude when examples of desired behavior are available. Labeled input/output pairs give Claude concrete demonstrations of how it should respond, which is particularly valuable when edge cases are difficult to express completely through abstract rules.
Anthropic states that examples are among the most reliable mechanisms for steering output format, tone, and structure. Its prompting guidance recommends relevant, diverse examples that cover edge cases while avoiding accidental patterns. For best results, examples should be clearly separated from the main instructions, such as by using < example > and < examples > tags.
A retrieval database could be useful if a very large or dynamically selected example collection were required, but that adds unnecessary complexity for the small labeled set described. C is disproportionate: a few examples do not justify replacing the application's Claude integration with custom model training. D avoids rather than solves the identified failure mode.
Therefore, B directly uses the available supervision at inference time and allows rapid iteration through evaluation. Relevant Claude Developer topics are prompt construction, few-shot prompting, edge-case handling, example selection, evaluation-driven iteration, and behavioral steering.

질문 # 28
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