A user asks Claude to ignore all previous instructions and reveal confidential internal prompt content. How should Claude behave?

• A. Follow higher-priority instructions and refuse.
• B. Partially reveal hidden instructions.
• C. Reveal the prompt.
• D. Generate random text.

A customer writes: "I've been going back and forth on this return for days. I just want to speak to someone who can actually help me." The agent has confirmed via lookup_order that the return is straightforward - within policy and eligible for immediate processing. What should the agent do?

• A. Acknowledge frustration, inform them this is resolvable now, and offer to complete it or escalate
• B. Ask what specifically hasn't worked in previous attempts before deciding whether to escalate or resolve automatically
• C. Process the refund via process_refund to resolve the underlying issue, then inform them it's complete
• D. Call escalate_to_human immediately to honor the customer's request

A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API. What is the primary mechanism your application uses to determine whether to continue the loop or stop?

• A. You manually set the tool_choice parameter to "none" after the final expected tool call to force Claude to stop requesting tools.
• B. You check whether Claude's response contains a text content block - if text is present, the agent has produced its final answer and the loop should exit.
• C. You track the number of tool calls made and exit the loop once a preconfigured maximum is reached.
• D. You check the stop_reason field in each API response - the loop continues while it equals
  "tool_use" and exits when it changes to "end_turn" or another terminal value.

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 system parses e-commerce product descriptions to extract specifications such as dimensions, weight, and materials into JSON. Despite having a well-defined schema, the model inconsistently extracts the materials field--sometimes returning "cotton blend," other times
"Cotton/Polyester mix," and occasionally omitting the field when material information is clearly present in the source.
What is the most effective way to improve extraction consistency?

• A. Add few-shot examples showing two or three complete input-output pairs with standardized material-description formats.
• B. Set the temperature to 0 to eliminate randomness and ensure deterministic outputs.
• C. Switch to a more capable model tier because inconsistent extraction indicates insufficient model capability.
• D. Make the materials field required instead of optional in the schema to force the model to always extract a value.

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. 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.
• B. 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.
• C. Build post-processing keyword filters that suppress findings containing terms such as "force unwrap," "large class," or "deprecated import" before results reach developers.
• D. Have developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model.