CCDV-F関連合格問題 & CCDV-F資格練習

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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Tools and MCPs	10.6%	- Model Context Protocol - Tool Use and Tool Schemas - Building Custom Tools and MCP Servers
Topic 2: Prompt and Context Engineering	11%	- Context Management and Long-Context Techniques - Prompt Engineering - Context Engineering
Topic 3: Agents and Workflows	14.7%	- Agent Patterns and Frameworks - Agent Construction with Claude - Agent Architecture
Topic 4: Applications and Integration	33.1%	- Message Batches and Prompt Caching - Multimodal and Structured Outputs - Claude API and Client SDKs - Streaming, Error Handling and Reliability - API Integration and Application Development - Software Engineering Fundamentals
Topic 5: Model Selection and Optimization	16.8%	- Model Selection - Cost and Latency Optimization - Performance Optimization - Model Capabilities and Trade-offs
Topic 6: Security and Safety	8.1%	- Safety and Responsible Development - Application Security - Secure Tool Use and Guardrails - Prompt Injection and Untrusted Content
Topic 7: Claude Code	3.1%	- Claude Code Configuration and Extensibility
Topic 8: Eval, Testing, and Debugging	2.6%	- Evaluation, Testing, and Debugging

>> CCDV-F関連合格問題 <<

CCDV-F資格練習 & CCDV-Fブロンズ教材

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Anthropic Claude Certified Developer-Foundations 認定 CCDV-F 試験問題 (Q49-Q54):

質問 # 49
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. Tell users to avoid submitting the edge-case inputs to the application by adding warnings in the application's user interface.
B. Add the labeled edge-case examples to the prompt as few-shot examples so the model can learn the pattern.
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. Embed the examples in a database for the model to find during inference.

正解：B

解説：
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.

質問 # 50
Your Claude agent performs database operations. A recent incident occurred where the agent ran a destructive query that affected production data. The team wants to add deterministic controls to prevent similar incidents.
How would you prevent similar incidents?

A. Switch to a higher-capability Claude model on the grounds that a more capable model is less likely to run destructive queries during normal operation across all requests.
B. Run the agent only during business hours when humans are available to monitor its activity, treating the schedule as the primary control mechanism for destructive operations.
C. Add Claude hooks that intercept database operations and apply deterministic checks, such as blocking destructive queries or requiring approval, before the queries execute.
D. Add a system prompt instruction telling the agent to be careful with database operations on every request the application handles during normal operation across all incoming traffic.

正解：C

解説：
Option B is correct because destructive production operations require deterministic enforcement outside the model's probabilistic reasoning. Claude Code hooks can intercept lifecycle events before tool execution and explicitly allow, deny, or request further handling based on concrete rules.
Anthropic's hooks documentation provides this exact security pattern. A PreToolUse hook can inspect a proposed command before execution and return a blocking decision. Anthropic's example demonstrates blocking destructive operations such as drop table, while other commands proceed normally.
That mechanism can be adapted to database controls: block DROP, destructive DELETE, unauthorized schema modifications, or production writes; require explicit approval for high-risk operations; and allow read- only or known-safe queries automatically.
A merely increases the probability that someone might notice an unsafe operation and does not prevent execution. C assumes model capability can replace access controls, which is an unacceptable safety boundary.
D is useful behavioral guidance but remains probabilistic and cannot guarantee prevention.
Therefore, B creates a deterministic control between model intent and side-effect execution. Relevant Study Guide topics: Claude hooks, PreToolUse, tool governance, deterministic enforcement, approval gates, least privilege, and destructive-operation protection.

質問 # 51
Your Claude application uses tool calling to fetch patient data and generate summary reports. The flow occasionally fails because the model returns a tool_use block that references arguments not present in the schema, and your application code does not handle this case gracefully.
How would you address this?

A. Log invalid tool_use blocks when they occur and allow the tool dispatch to proceed, relying on the tool's own error handling to surface failures back to the application.
B. Validate the tool_use block's arguments against the tool schema before dispatching the tool and handle invalid arguments as a recognized error path.
C. Retry the same request repeatedly until the model returns a valid tool_use block that matches the schema as expected.
D. Stop using tool calling entirely and replace tools with prompted text generation that asks the model to describe what it would do.

正解：B

解説：
The question presents a trust-boundary failure: model-generated tool arguments are being accepted without sufficient validation before an external operation executes. A tool call should therefore be treated like input arriving at any typed application interface-the application must ensure that the tool name and arguments satisfy the declared contract before dispatch.
Anthropic describes tool use as a contract in which the application defines the tool's schema and Claude returns structured tool_use input. Current Claude functionality also supports strict: true, which guarantees schema-compliant tool inputs through constrained generation and specifically addresses invented parameters, incompatible types, and missing required fields. For applications not using strict tool use, explicit application- side validation and a controlled error path remain essential.
B knowingly executes invalid input. C relies on uncontrolled repetition rather than deterministic validation and bounded recovery. D eliminates a useful capability instead of correcting its execution boundary.
Therefore, A provides the correct defensive design. Relevant Study Guide topics: tool calling, tool_use, input_schema, strict tool use, schema validation, defensive execution, error handling, and secure tool dispatch.

質問 # 52
You are designing a Claude application that processes user-submitted text. Some of that text could include sensitive information such as account numbers or passwords that the application should not send to Claude.
How would you design the application?

A. Log all user-submitted text before it is sent to Claude and review the logs periodically to identify whether sensitive information is reaching the model.
B. Add a prompt instruction in the system prompt specifying the categories of sensitive information Claude should disregard when processing user-submitted text.
C. Define the application boundary explicitly, identify what content can leave the boundary for Claude, and add filtering or redaction at the boundary.
D. Apply filtering at the boundary for the most commonly observed sensitive data patterns and expand coverage to additional patterns based on findings from production monitoring.

正解：C

解説：
Option A is correct because sensitive-data protection must occur before prohibited information crosses the application's trust boundary. If account numbers, passwords, or other secrets must not be sent to Claude, asking Claude to "ignore" those values after transmission does not satisfy the requirement. The application must determine which data is permitted to leave its controlled environment and transform or reject prohibited content before constructing the Claude request.
This principle is consistent with Anthropic's defense-in-depth guidance. Anthropic recommends input screening, limiting Claude's access to sensitive information, and applying least privilege so the model receives only information necessary for the task.
B is fundamentally too late: the sensitive information has already been transmitted to the model context before the system instruction can influence behavior. C is worse because logging can create an additional store containing the sensitive information and only detects exposure after it occurs. D implements some boundary filtering but deliberately leaves coverage incomplete until production incidents reveal additional patterns.
Therefore, A requires the boundary and its permitted data flows to be defined first, then enforced comprehensively through filtering, redaction, tokenization, or rejection. Relevant Study Guide topics:
application boundaries, data minimization, sensitive-data handling, filtering, redaction, least privilege, and privacy-preserving design.

質問 # 53
You are setting up a Claude application that requires API keys for several external services.
What is the best way to store the keys?

A. Store the keys in a secrets manager or environment-specific configuration that is not checked into source code, and load them at runtime.
B. Email the keys to each developer as needed and have each developer paste the keys into their local environment when they begin working on the application's code.
C. Use a single shared key across all external services, so any developer working on the application can find the keys easily during development.
D. Put the keys in the application's configuration file and check the configuration file into the team's repository alongside the rest of the source code.

正解：A

解説：
Option B follows standard secrets-management practice and Anthropic's explicit guidance for API credentials.
API keys are authentication secrets and should not be embedded in source code or committed to repositories.
Anthropic's authentication documentation explicitly recommends storing API keys in a secrets manager, rotating them periodically, and revoking credentials suspected of compromise. Anthropic SDKs can load Claude credentials from environment variables such as ANTHROPIC_API_KEY, allowing the secret to be injected at runtime instead of compiled into the application.
The same principle applies to external service credentials used by a Claude application. Development, staging, and production should normally receive independently scoped credentials through the deployment environment or secret-management infrastructure.
A creates a high-probability credential leak because repository history can retain secrets even after a later deletion. C violates isolation and least privilege by sharing credentials across services and users. D uses email as an uncontrolled secret-distribution channel and creates inconsistent manual configuration.
Therefore, B supplies credentials only at runtime while keeping them outside source control and enabling rotation and environment-specific access. Relevant Study Guide topics: API-key management, secrets managers, environment configuration, credential rotation, least privilege, and secure configuration.

質問 # 54
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