Took me a minute to realize the credit math is just summing (max_usage minus actual_usage) for each hour, positive means you banked credits, negative means you spent them.
Clarify the rules for credit accrual and consumption, then iterate through the hourly usage list, comparing each value to the maximum available CPU. Accumulate credits when usage is below the max and deduct when above, handling edge cases like exactly at max and negative totals.
Pro tip: Mention that this is similar to Amazon EC2 CPU credit model and discuss how you would handle large input streams efficiently, showing awareness of real-world constraints.
Ask about the credit rate (e.g., 1 credit per CPU-hour below max), whether credits can go negative, and if there's a cap on accrued credits. Confirm input format and expected output.
For each hourly usage, compute the difference (max - usage). If positive, add to total credits; if negative, subtract. Keep a running total.
Consider usage exactly equal to max (no change), empty list, all usage above max (total negative), and potential integer overflow for large lists.
Write clean code with a loop, then test with sample inputs including edge cases. Discuss time and space complexity (O(n) time, O(1) space).
Mention how to handle streaming data, parallelization for large datasets, or variations like different credit rates per hour.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.