Your team uses several plugins across multiple Claude applications, and a recent plugin update introduced a regression. The team had not been tracking plugin versions, so the team cannot easily identify which version was previously working. How would you address this?

• A. Upgrade every plugin to the latest version on a regular cadence to keep version drift small, on the grounds that drift contributes to regression risk.
• B. Stop using all plugins until the team can rebuild equivalent functionality directly into the application code, treating plugin avoidance as a way to remove version-related risk.
• C. Add explicit plugin version tracking to the project's configuration so the team can identify, pin, and upgrade plugin versions deliberately.
• D. Treat plugins as untrackable third-party code and rely on plugin authors to communicate breaking changes when they happen, with no internal version tracking.

Your Claude application returns confident-sounding answers, but occasionally those answers contain factual errors that downstream systems treat as ground truth. The team is concerned about the application's confidence-versus-accuracy gap.
How would you address the gap?

• A. Lower the model's temperature so the model's responses sound less confident and downstream systems are less likely to treat the responses as ground truth in normal operation.
• B. Reject every response the application produces until a manual accuracy review is conducted on each response by a human reviewer before any downstream system uses it.
• C. Apply skepticism toward confident output by adding validation steps, sourcing requirements, or confidence calibration before treating outputs as ground truth.
• D. Add a disclaimer to every output telling users to verify the accuracy of the output and treat the disclaimer as the primary mechanism for managing the confidence-versus-accuracy gap.

Your Claude agent's hooks are currently triggered for every action, which slows down the agent significantly even when actions pose no risk. The team wants to scope hooks more carefully.
How would you scope the hooks?

• A. Replace hooks with system prompt instructions on the grounds that prompt instructions can produce the same enforcement effect that hooks produce on the agent's actions.
• B. Disable the agent during peak hours so the hook overhead does not slow the application down during the busiest periods of the day across the application's operation.
• C. Scope hooks to only the high-risk actions, such as destructive operations or sensitive data access, and remove hooks from low-risk actions to balance safety with performance.
• D. Disable all hooks while the team re-scopes them, treating the period of no hook enforcement as a temporary state during the re-scoping work.

A teammate has asked you to explain when a Skill would be the right choice over an MCP server. The teammate is unsure how the two differ in practice when both can be reused across teams.
How would you explain the distinction?

• A. A Skill is preferable for cross-team reuse because it loads more efficiently than an MCP server during normal operation in the team's typical multi-team workloads.
• B. A Skill and an MCP server are equivalent extension mechanisms that the team can use interchangeably for any reusable capability that needs to be accessible across teams.
• C. A Skill is the older mechanism and an MCP server is the newer one, so the team should prefer an MCP server for any reusable capability that the team builds going forward.
• D. A Skill bundles prompts, scripts, and data into a package the model loads as a unit while an MCP server exposes resources, tools, and prompts through a standard client interface.

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. Stop using tool calling entirely and replace tools with prompted text generation that asks the model to describe what it would do.
• C. Retry the same request repeatedly until the model returns a valid tool_use block that matches the schema as expected.
• D. Validate the tool_use block's arguments against the tool schema before dispatching the tool and handle invalid arguments as a recognized error path.