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. Assess the configuration impact on each downstream system before rolling out, and coordinate with downstream system owners as needed.
• 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. Limit the rollout to systems that were explicitly included in staging testing, and defer all other downstream systems until a later release cycle.
• D. 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.

The product team has asked you to choose a Claude model for a new feature. The team has provided functional requirements but has not specified performance, cost, or quality targets. The team's product manager says, "Use whatever model gives us the best results." How would you respond?

• A. Choose a mid-tier model and ship the feature, because mid-tier models work for most use cases without specified targets.
• B. Run every Claude model on a representative sample and pick whichever scores best on a generic benchmark.
• C. Ask the product team to specify quality, latency, and cost targets, then select the model whose tradeoffs best fit those targets.
• D. Choose the largest, highest-capability Claude model, on the grounds that "best results" is most likely to mean highest quality.

A teammate has asked why your Claude application sometimes produces a response that includes the prompt text repeated back, and other times produces a response with the prompt text rephrased. They suspect a bug in the application's request construction.
How would you respond?

• A. Tell the teammate that the variation is caused by the application sending two different prompts on different runs and propose a code change that pins the system prompt to a single version.
• B. Explain that LLMs generate output token by token, and variation in how prompt content appears in output is a property of generation, not a bug in request construction.
• C. Tell the teammate that the variation depends on which Claude model serves the request, and recommend pinning the application to a single model version to make the output behavior consistent.
• D. Confirm that the variation is a bug in the application's request construction and start investigating which part of the application is producing the inconsistent prompt text.

You are building a Claude application that needs to maintain a persistent connection to a service that streams real-time updates. The team is unsure what communication pattern to use.
Which communication pattern would you use?

• A. Repeated short-lived HTTP polling requests, where the application opens a new HTTP connection each time it checks for updates.
• B. A WebSocket, because WebSockets are designed for bidirectional, persistent, real-time communication between the client and the streaming service.
• C. File-based communication where the service writes new updates to disk and the application polls the file system for changes.
• D. A single long HTTP request the server holds open indefinitely, with no standard WebSocket framing on the connection.

You are extending a Claude agent with a capability that needs to be reusable across multiple teams in the organization, with each team able to invoke and use it independently.
How would you build the capability?

• A. As a Skill or MCP server because both are purpose-built for cross-team reuse independently by each consuming team.
• B. As a custom tool embedded in this team's agent only, with other teams able to copy the implementation into their own agents when they need the capability.
• C. As a wrapper around an existing built-in tool that adds the missing functionality, on the grounds that built-in tools cover the reuse pattern when extended carefully.
• D. As a shared library that each team imports into its own Claude application code, with each team responsible for keeping the library up to date in its integration.