Your Claude application's error handling currently logs every API error with the same severity level. The team wants to differentiate between errors that should page an on-call engineer and errors that should be logged for later review. How would you structure the error handling?

• A. Page on every error, on the grounds that paging guarantees that no error is missed by the team during normal operation across the application's lifecycle.
• B. Log every error with the same severity, on the grounds that differentiating severity adds complexity that does not pay off in most application setups over time.
• C. Categorize errors by severity based on impact and recoverability, then route each category to the appropriate channel for paging or logging.
• D. Disable logging for any error that does not page, treating non-paging errors as not worth recording for later review either.

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The Claude application your team built has grown over six months, and the prompt-handling code has accumulated duplication and tangled control flow. The functionality is working, but new features are getting harder to add.
How would you address this?

• A. Plan a refactoring pass to consolidate duplicated logic, separate concerns, and simplify control flow before adding new features.
• B. Continue adding features and plan a refactoring pass after the next two release cycles when the team has more bandwidth for internal work.
• C. Refactor the prompt-handling code in small increments as part of each new feature ticket, treating the cleanup as a side effect of feature work.
• D. Move all the prompt-handling code into a single large function to reduce the number of files developers have to navigate when reading the code.

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You are deciding between Claude models for a task. The team has identified three relevant tradeoff dimensions: quality, latency, and cost.
The right model is the one that...

• A. Meets the task's quality requirements at an acceptable latency, with cost reviewed separately once the quality and latency bar has been established.
• B. Satisfies the task's latency requirement first, then is evaluated against quality and cost thresholds to confirm the selection is acceptable across all three dimensions.
• C. Fits the task's quality, latency, and cost requirements together, recognizing that improving one dimension typically affects the others.
• D. Meets the task's cost target within a defined latency budget, with quality validated against a representative sample of inputs after the model is selected.

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

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Your Claude application uses structured output that is consumed by downstream code. The team wants to handle malformed or unexpected output gracefully so it does not crash downstream systems.
The best choice for handling this issue would be to...

• A. Use the output the model produces and leave schema enforcement to a later phase of production after the application is more complete.
• B. Silently drop any output that does not match expectations so downstream systems do not see the malformed output the application receives from the model.
• C. Crash on any unexpected output and require manual recovery on affected requests the application handles during normal operation.
• D. Apply defensive parsing to handle unexpected fields, missing values, and type mismatches that the downstream code might otherwise crash on.

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