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Anthropic CCDV-F Exam Syllabus Topics:

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

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最新的 Claude Certified Developer CCDV-F 免費考試真題 (Q80-Q85):

問題 #80
A Claude application is occasionally refusing to answer questions that should be in scope, including questions the application has answered correctly in the past. You want to investigate.
What is the first step of your investigation?

A. Test whether removing or adjusting those instructions resolves the behavior.
B. Examine traces of the refused requests to identify what triggers the refusal, whether input patterns, system prompt content, or other context.
C. Identify which questions have been refused and compare them against previously answered questions to determine what changed between the successful and refused interactions.
D. Assess whether the refusals cluster around specific input patterns, question types, or time periods by analyzing the distribution of refused requests in the logs.

答案：B

解題說明：
The supplied exam source marks B as correct. The first debugging action should be evidence collection rather than immediately modifying prompts or configuration. A trace exposes the complete conditions under which the refusal occurred: user input, system instructions, accumulated conversation state, tool results, parameters, and resulting model output.
This is important because a refusal can originate from several interacting causes. A particular input pattern may trigger it; system instructions may unintentionally conflict with the requested task; prior conversational context may alter Claude's interpretation; or contextual information may make an otherwise acceptable request appear out of scope. Without examining the failing execution path, changing instructions is speculative.
Option A jumps directly to remediation before identifying the cause. C provides useful aggregate analysis but is more appropriate after individual failing executions have been inspected. D is also valuable for comparative debugging, but determining what changed requires the detailed execution data that traces provide.
The correct troubleshooting sequence is therefore: capture or inspect representative failed traces, determine the trigger, compare against successful interactions, formulate a hypothesis, make a targeted change, and re- evaluate.
Relevant Claude Developer topics: Systems Life Cycle, observability, production debugging, tracing, failure analysis, refusals, regression investigation, and evaluation-driven remediation .

問題 #81
Your Claude application is deployed to development, staging, and production environments. Each environment uses a different model version, different prompt versions, and different plugin dependencies, but the configuration is currently scattered across environment variables, hardcoded values, and undocumented setup scripts.
How would you manage the configuration?

A. Use the latest available model version everywhere and stop pinning model versions, on the grounds that pinning adds maintenance overhead the team should aim to reduce.
B. Consolidate the configuration into a version-controlled system documenting model version pinning, prompt versioning, and plugin dependencies for each environment.
C. Standardize all environments to use the same configuration values to eliminate the differences between development, staging, and production.
D. Move all configuration into hardcoded application code to reduce reliance on external configuration sources that are difficult to track over time.

答案：B

解題說明：
Option C provides the required configuration-management discipline. Development, staging, and production may legitimately use different models, prompts, plugins, permissions, or service endpoints, but those differences must be explicit, reproducible, and auditable rather than scattered across undocumented mechanisms.
Claude Code documentation follows the same configuration-as-code principle. Project-level configuration can live in source-controlled files such as .claude/settings.json, while project instructions are maintained in repository-level CLAUDE.md. Anthropic specifically distinguishes shared project settings from local developer configuration.
A introduces uncontrolled model changes and regression risk. B hides configuration in application logic and makes environment differences harder to review. D ignores the fact that environments often require deliberate differences-for example, production credentials or pinned release versions.
The correct strategy is therefore to define configuration centrally, pin compatibility-sensitive dependencies, record environment-specific overrides, review modifications through source control, and retain rollback history. Relevant Study Guide topics: configuration management, environment isolation, model versioning, prompt versioning, dependency management, reproducibility, and controlled deployment.

問題 #82
You are designing a Claude application that helps medical researchers analyze multi-step clinical case studies.
The application must work through differential diagnoses by considering symptom patterns, weighing evidence across competing hypotheses, and showing intermediate reasoning steps before producing a final recommendation. The team is choosing among Claude's available model options.
The model option best suited to this use case is...

A. A smaller model with a tighter context window, which encourages the model to focus its limited capacity on the task.
B. Fast mode, which prioritizes the lowest possible latency at the expense of reasoning depth on complex tasks.
C. Zero-shot prompting alone with no model option adjustments, which keeps the application's configuration as simple as possible.
D. Extended thinking, which lets the model reason through the differential diagnosis steps before producing the final recommendation.

答案：D

解題說明：
Option B is the best answer because the task requires deliberate multi-step reasoning over competing hypotheses before a final recommendation. Anthropic's thinking documentation explains that enabling thinking gives Claude additional reasoning capacity before producing the final response, and larger thinking budgets can improve performance on complex analytical tasks. That maps directly to a differential-diagnosis workflow involving evidence comparison, hypothesis elimination, and multi-stage synthesis.
Option A uses ordinary zero-shot prompting and does not specifically allocate additional reasoning capacity.
Option C explicitly optimizes for minimum latency at the expense of reasoning depth, which conflicts with the complexity requirement. Option D reduces model and context capacity and therefore has no principled relationship to better differential reasoning.
One current-platform nuance is important: manual "extended thinking" is the legacy configuration on supported Claude 4.5/4.6 models, while newer models use adaptive thinking. That does not change the question's underlying answer: among the listed choices, B is the only option that intentionally provides a reasoning phase for complex analysis. Relevant Study Guide topics: thinking, reasoning budgets, complex- task model configuration, latency-quality tradeoffs, and model selection.

問題 #83
Your team is preparing to roll out a configuration change that updates several prompt versions across a Claude application used by multiple downstream systems. The change has already been tested in staging, but the team has not assessed how the prompt change will affect each downstream system that depends on the application's output.
What would you do before rolling out the change?

A. Limit the rollout to systems that were explicitly included in staging testing, and defer all other downstream systems until a later release cycle.
B. Notify downstream system owners that a change is coming and schedule the rollout for the following week, without conducting a formal impact assessment.
C. Document the prompt version changes in the application changelog and proceed with the rollout, treating the staging test results as sufficient evidence of impact across all downstream systems.
D. Assess the configuration impact on each downstream system before rolling out, and coordinate with downstream system owners as needed.

答案：D

解題說明：
A is the appropriate configuration-change control. The supplied examination item selects A . A prompt is not merely editorial text; in a Claude application it functions as executable behavioral configuration. Changing a prompt can alter output structure, field population, language, classifications, tool-use decisions, refusal behavior, or other assumptions on which downstream consumers depend.
Staging success therefore proves only the scenarios actually covered by staging. Before production rollout, the team must perform impact analysis across every dependent system, identify contractual expectations, run representative regressions, and coordinate changes where a consumer may be affected. This is especially important where downstream applications parse structured output or expect stable semantics.
Anthropic's Structured Outputs guidance illustrates why interface contracts matter: missing fields, inconsistent types, and schema violations can break consuming applications. Even when output remains syntactically valid, prompt changes can produce semantic changes that require consumer validation.
B assumes staging coverage is universal. C communicates the change without determining its consequences.
D arbitrarily defers systems instead of assessing them.
Relevant Claude Developer topics: Confia Management, configuration impact assessment, prompt versioning, dependency management, change control, downstream contracts, regression testing, and coordinated deployment .

問題 #84
You are implementing a custom tool for your Claude agent. The tool needs to interact with an external pricing service that returns product data.
Which of the following best practices would you apply as you develop this tool?

A. Define the tool with a loose schema and let the model interpret the inputs flexibly on each call the agent makes.
B. Define the tool with a clear schema, write a precise description for when to call it, and handle pricing service errors explicitly.
C. Implement the tool with no error handling and let the agent loop catch failures whenever the pricing service returns an error during operation.
D. Omit the tool description and let the model infer when to use the tool based on the tool's name and the rest of the prompt context.

答案：B

解題說明：
Option D combines the three key properties of a reliable Claude tool: an explicit contract, clear tool-selection guidance, and controlled execution failure handling. Anthropic's tool documentation defines user tools using a name, detailed description, and JSON input_schema. The description should explain what the tool does, when it should and should not be used, parameter semantics, and relevant limitations. Anthropic emphasizes that precise descriptions materially improve Claude's ability to select the correct tool.
A clear schema prevents ambiguous parameter interpretation and allows validation before calling the external pricing API. Where stronger guarantees are required, Anthropic also supports strict tool use, which constrains generated tool inputs to the declared JSON Schema.
The application's execution layer must also convert pricing-service failures into explicit, handled error paths rather than uncontrolled exceptions. A deprives Claude of critical selection information. B increases malformed-call risk. C delegates infrastructure reliability to the reasoning loop instead of implementing appropriate integration error handling.
Therefore, D represents production-quality custom-tool construction. Relevant Study Guide topics: custom tools, JSON Schema, tool descriptions, validation, external API integration, and error handling.

問題 #85
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CCDV-F考試證照綜述: https://www.kaoguti.com/CCDV-F_exam-pdf.html