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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Model Selection and Optimization	16.8%	- Performance and Cost Optimization 1. Use batching and other approaches to improve efficiency 2. Apply prompt caching and other cost optimization techniques - Model Selection 1. Select appropriate Claude models for task requirements 2. Evaluate quality, latency, capability, and cost tradeoffs
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
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. Test Claude integrations and agentic systems 2. Diagnose and debug application, agent, and integration issues
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
Tools and MCPs	10.6%	- 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 Context Protocol 1. Build and integrate MCP servers 2. Apply MCP concepts and patterns for connecting models to external capabilities
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
Applications and Integration	33.1%	- Application Development and Integration 1. Build and ship production-grade Claude-powered applications 2. Integrate Claude capabilities into existing software systems and workflows 3. Handle multimodal and structured application inputs and outputs - 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
Agents and Workflows	14.7%	- Subagents and Agentic Frameworks 1. Apply appropriate agentic frameworks and orchestration patterns 2. Use subagents and coordinate multi-agent workflows - 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

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최신 Claude Certified Developer CCDV-F 무료샘플문제 (Q67-Q72):

질문 # 67
Your team's Claude agent has accumulated several customizations that bypass the SDK's defaults, including custom history management, retry logic, and error handling. A new team member has proposed reverting all the customizations to maintain the codebase more easily. The tech lead disagrees and says each customization was added for a reason.
How would you advise the team?

A. Migrate the agent off the SDK and rebuild it with a custom loop.
B. Revert all customizations to the SDK's defaults to standardize the codebase.
C. Keep all customizations, trusting that the tech lead's original reasoning is still valid.
D. Decide on each customization individually based on its original reason and the SDK's current capabilities.

정답：D

설명：
The correct approach is to evaluate each customization independently against both its original requirement and the capabilities currently supplied by the Claude SDK. Custom code has an ongoing maintenance cost, but automatically removing it is unsafe because some modifications may encode genuine application requirements that the standard SDK behavior still does not address. Conversely, automatically retaining every customization can preserve obsolete complexity after the SDK has evolved.
This is particularly relevant because Anthropic's SDKs already provide capabilities that developers might otherwise implement themselves. Official Claude API documentation states that SDKs provide built-in request formatting, error handling, retries, timeouts, and other integration functionality. For example, transient connection, rate-limit, and server errors are retried automatically with exponential backoff.
Therefore, each custom history, retry, or error-management layer should be reviewed for its actual purpose. If an SDK primitive now provides equivalent behavior, removing duplicate code improves maintainability. If the customization still satisfies a distinct requirement, it should remain.
The supplied exam source explicitly identifies D as the intended answer. Relevant topics: Agent Construction, SDK capabilities, abstraction boundaries, technical-debt management, retries, error handling, and agent state management.

질문 # 68
You are explaining to a stakeholder why running the same Claude prompt twice can produce slightly different results. The stakeholder is concerned this means the application is broken.
How would you address the stakeholder's concern?

A. Tell the stakeholder the variation is a bug that the team will fix in the next release of the application, then create a work ticket to fix the bug.
B. Tell the stakeholder the variation is caused by Claude being updated continuously by Anthropic, and that switching to a fixed model snapshot will eliminate the variation entirely.
C. Tell the stakeholder the variation comes from network latency and that switching to a faster network connection will produce more consistent results across runs.
D. Explain that LLMs are non-deterministic by default due to sampling, and describe how the application handles this through validation, retries, or temperature adjustment.

정답：D

설명：
Option C correctly explains a fundamental property of generative language models. The supplied examination material identifies C as the correct answer. Claude generates subsequent tokens probabilistically rather than retrieving a single fixed answer for each prompt. Consequently, identical or highly similar requests can produce variations in wording, ordering, explanation depth, and sometimes substantive details.
Anthropic's API documentation explicitly states that sampling parameters control randomness and, importantly, that even configurations historically using a temperature of 0.0 were not fully deterministic .
Current newer Claude models increasingly manage sampling behavior internally, so application design should not assume byte-for-byte identical responses across executions.
Production systems therefore handle variation through architecture: schema validation for machine-consumed output, deterministic business-rule checks, retries where appropriate, evaluations for acceptable behavioral ranges, and application-level safeguards. Temperature adjustment may reduce variation on models that support the parameter, but it does not fundamentally convert an LLM into a deterministic function.
A incorrectly labels normal model behavior as a defect. B confuses network latency with generation variability. D overstates model snapshot behavior; pinning a model prevents silent model-version changes but does not eliminate sampling variability.
Relevant topics: sampling, nondeterminism, validation, retries, model versions, and robust Claude API integration .

질문 # 69
Your agent makes 10 to 15 tool calls per task, and you have noticed it sometimes loses track of earlier results by the time it reaches later steps. The agent's context window is large enough to hold all the messages, but the relevant information appears to get buried as the conversation grows.
How would you address this?

A. Reduce the number of tool calls per task by combining several existing tools into larger, multi-purpose tools.
B. Switch to a different agentic framework that advertises automatic context-window management as a built-in feature.
C. Increase the context window further so all tool outputs from every prior step remain in full detail throughout the task.
D. Apply a context-management pattern that summarizes or prunes older tool outputs while preserving the active task state.

정답：D

설명：
Option D is correct because the problem is not insufficient nominal context capacity; it is degraded signal quality as the working context grows. Anthropic's context-window guidance explicitly states that more context is not automatically better. As conversations expand, recall and accuracy can degrade through
"context rot," so long-running agents need active context management rather than simply retaining every prior tool result.
A good pattern preserves the active task state while compacting, summarizing, or pruning stale intermediate outputs. Anthropic documents server-side compaction for long-running conversations and context-editing
/pruning approaches that clear old tool results when they no longer contribute useful information. This keeps high-value constraints and conclusions visible without carrying every verbose response forward.
Option A changes frameworks without fixing the architectural cause. Option B may reduce call count but also creates larger multi-purpose tools and does not guarantee better context quality. Option C increases capacity while preserving the same low-signal accumulation, so the relevant facts can remain buried.
Therefore, D best implements context engineering for agentic workloads. Relevant Study Guide topics:
context engineering, context rot, compaction, tool-result pruning, long-running agents, state preservation, and context-window optimization.

질문 # 70
Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?

A. Adjust the temperature parameter together with the max_tokens parameter, treating the combined adjustment as the team's mechanism for managing context window pressure during long-transcript processing.
B. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
C. Remove the system prompt entirely to make room for longer transcripts in each request, freeing up context window space the system prompt would otherwise consume.
D. Increase the temperature parameter as the junior developer suggested and observe whether the context window issue resolves over the next several runs of the application in production.

정답：B

질문 # 71
You are choosing a Claude model for a high-volume classification task. Each classification is straightforward, latency requirements are tight, and per-request cost matters at scale.
Which model would you choose?

A. Multiple Claude models in series, where each request runs through more than one model and the application combines the outputs into a final classification.
B. A smaller, faster Claude model, because the task is straightforward and the workload prioritizes latency and per-request cost at scale.
C. The largest, highest-capability Claude model, to maximize quality on every classification the application produces during normal operation across all requests.
D. A mid-tier Claude model selected by default, because mid-tier models balance quality and cost in a way the team can apply across most tasks.

정답：B

설명：
Option C matches Anthropic's efficiency-first model-selection guidance. For a straightforward, high-volume workload where latency and unit cost are explicit constraints, the correct starting point is a faster, economical model that can meet the task's quality threshold. Anthropic specifically lists high-volume straightforward tasks, tight latency requirements, and cost-sensitive implementations as cases where an efficiency-first model choice is appropriate.
The crucial qualification is that "smaller" does not mean accepting inadequate quality. The team should evaluate the candidate against representative classification examples and defined accuracy criteria. If it passes, moving to a larger model adds cost and often latency without delivering required business value.
A selects a mid-tier model by convention rather than workload evidence. B multiplies inference calls, generally increasing both latency and cost for a simple classification problem. D optimizes maximum capability even though the task does not require frontier-level reasoning.
Therefore, C is the appropriate initial model choice, followed by workload-specific validation. Relevant Study Guide topics: Claude model selection, efficiency-first design, classification workloads, throughput, latency, per-request economics, evaluation, and quality/cost tradeoffs.

질문 # 72
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