Your team's Claude application has been in production for a year, and the team has decided to formalize its testing strategy. Currently, the team writes ad-hoc tests for individual features but has no overall testing approach.
What testing approach would you formalize?

• A. Define unit tests for individual functions, integration tests for the Claude integration, and end-to-end tests for critical user flows, applied consistently across the codebase.
• B. Continue writing ad-hoc tests as features ship and introduce a peer review step to ensure each test adequately covers the feature being released.
• C. Define a single testing approach that uses end-to-end tests and apply it consistently across all new features as they are added to the codebase.
• D. Adopt a test-driven development practice where unit tests are written before each feature is implemented and must pass before code is merged.

A new agent your team built handles customer support tickets, but it routinely gets confused when a single ticket spans billing, shipping, and product issues. The agent often loses track of which sub-issue it has already addressed and revisits the same one. The team is considering architectural changes.
What architectural change would you recommend?

• A. Switch to a deterministic workflow that handles billing, shipping, and product issues in a fixed sequence.
• B. Increase the size of the agent's context window so it can hold the full ticket history at once.
• C. Add detailed prompting that instructs the agent to track which sub-issues have been resolved and which remain.
• D. Introduce an orchestrator agent that delegates billing, shipping, and product sub-issues to dedicated subagents.

Your Claude application makes high-volume API calls during business hours and very few calls overnight.
The team is concerned about staying within rate limits during peak hours and wants to understand how the Claude API enforces those limits.
How would you proceed?

• A. Assess the average payload size of current API calls and consolidate requests where possible to reduce the total number of calls made during peak hours.
• B. Examine the peak-hour request patterns in your application logs and smooth traffic by distributing requests more evenly across the business-hours window.
• C. Review the API documentation for streaming endpoints and evaluate whether migrating peak-hour calls to streaming reduces exposure to rate limit enforcement.
• D. Identify the rate limits, design the application to stay within them during peak hours, and use exponential backoff when limits are reached.

Your application uses the Messages API to handle multi-turn conversations. Each new turn resends the entire conversation history, and your token costs are growing as conversations get longer. You suspect there is a more efficient approach.
How would you address this?

• A. Truncate every conversation to the last two messages so that token usage stays low and costs remain predictable across the application's normal operation.
• B. Summarize each conversation after every turn and replace the full history with the summary on the next request, reducing token count at the cost of fidelity.
• C. Switch to the Batch API for every turn so the per-call cost is reduced, treating the batch as the team's primary cost-control mechanism for multi-turn work.
• D. Use prompt caching to reuse the static portions of the conversation context across turns instead of paying for them at the normal input-token rate on every request the application sends.

A new Claude model release includes performance improvements for several reasoning tasks but has changed the format of its responses to system prompts that use multi-section instructions. Your application uses multi- section system prompts heavily. Initial evaluation on the application's actual workload shows the new model performs 8 percent better on reasoning tasks but produces malformed output on roughly 3 percent of requests because of the format change. The team is debating whether to upgrade.
How would you decide?

• A. Upgrade and add a downstream validation step that catches the 3 percent malformed output before it reaches users, treating the validation step as the team's mitigation for the format change.
• B. Adapt the application's system prompt to the new model's format expectations and re-evaluate, then upgrade only if the adapted prompt eliminates the malformed output while preserving the reasoning improvements.
• C. Upgrade immediately, because the 8 percent reasoning improvement outweighs the 3 percent malformed output rate across the application's typical request distribution.
• D. Stay on the previous model permanently to avoid the malformed output rate and any future format changes that subsequent model releases might introduce in the application.