CCAR-F 試験問題を無料オンラインアクセス

Compliance requires that refunds exceeding $500 must automatically escalate to a human agent
- this rule cannot be left to model discretion. Despite clear system prompt instructions, production logs show the agent occasionally processes high-value refunds directly (3% failure rate). How should you achieve guaranteed compliance?

• A.Strengthen the system prompt with emphatic language: "CRITICAL POLICY: Refunds over $500 MUST trigger human escalation. NEVER process these directly."
• B.Modify the refund tool to return an error with message "Amount exceeds policy limit - please escalate" when threshold is exceeded.
• C.Add few-shot examples to the prompt showing correct escalation behavior at various refund amounts ($400, $500, $600).
• D.Implement a hook to intercept tool calls; when the refund process amount exceeds $500, block it and invoke human escalation.

You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
The automated review consistently flags patterns your team uses intentionally--force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK.
Developers dismiss approximately 30% of all findings as project-specific false positives.
Which approach prevents the model from generating these findings in the first place by supplying the project's conventions as persistent context during every review?

• A.Build post-processing keyword filters that suppress findings containing terms such as "force unwrap," "large class," or "deprecated import" before results reach developers.
• B.Have developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model.
• C.Document the team's accepted patterns and intentional conventions in the project's CLAUDE.md file so the model receives this context during every review.
• D.Configure the review to analyze only the changed lines in the diff without the surrounding file context, reducing the amount of code the model evaluates.

After deploying automated code review, developers report that approximately 35% of flagged findings are false positives falling into consistent patterns: style suggestions contradicting team conventions, security warnings for patterns that are safe in your deployment context, and performance suggestions that would degrade your specific use case. You want to reduce false positives while maintaining the ability to catch genuine issues. Which approach best enables the model to generalize its judgment to novel code patterns it has not seen before?

• A.Implement post-processing that uses keyword matching to filter out findings containing terms such as "convention," "context-dependent," or "trade-off."
• B.Include few-shot examples in your prompt showing annotated code snippets that distinguish acceptable patterns from genuine issues in each category.
• C.Create a comprehensive written specification of all patterns that should not be flagged, and then include the full documentation in the system prompt.
• D.Add instructions to your system prompt to "be conservative," "only flag definite issues," and
  "consider that some patterns may be intentional."

You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your pipeline includes a release-notes generation step that classifies and summarizes approximately 200 commits at the end of each weekly release cycle. Each commit is currently sent as a separate Messages API request using a Sonnet-tier Claude model. The release notes are not needed until the following morning, providing approximately 12 hours of acceptable latency.
Your team must reduce the per-token API cost while retaining the same model, prompts, and output quality.
Which approach satisfies all these constraints?

• A.Concatenate all 200 commit messages into one Messages API request because reducing the number of requests always reduces token costs.
• B.Replace the Sonnet-tier model with a Haiku-tier model to obtain a lower per-token price.
• C.Issue the 200 Messages API requests concurrently because parallel execution reduces the per- token price.
• D.Submit the 200 requests through the Message Batches API with unique custom_id values and retrieve the results after the batch finishes.

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline occasionally receives responses that cannot be parsed as valid JSON, causing downstream processing failures. The current implementation prompts Claude to return JSON in the response text and then parses it.
What is the most reliable approach to ensure Claude returns valid, schema-compliant structured data?

• A.Implement a retry loop that catches JSON parsing errors and re-prompts Claude with the error details, asking it to correct the malformed output.
• B.Use regular expressions to locate and extract JSON from the response text, handling cases where Claude includes explanatory text around the JSON block.
• C.Define a tool with a JSON schema specifying the expected structure, using tool use to constrain Claude's output to schema-compliant JSON.
• D.Add explicit formatting instructions to the prompt with JSON examples, emphasizing that Claude must return only valid JSON with no surrounding text.