I got the sliding window approach pretty quickly, keep a running sum, divide by N, compare against the threshold.
Clarify the requirements (N, threshold, handling of initial readings) and then design an efficient solution using a sliding window with a running sum to compute the rolling average in O(1) per reading. Iterate through the readings, maintain the window, and when a spike is detected, record the tuple (timestamp, temperature, baseline) where baseline is the rolling average.
Pro tip: Discuss the trade-off between using a fixed-size queue (O(N) space) and a more memory-efficient approach like a circular buffer or a running sum with a deque, and mention how you would handle edge cases such as fewer than N previous readings or missing data.
Ask about the definition of rolling average (e.g., simple moving average), what to do when there are fewer than N previous readings (skip, use available, or pad), and whether the threshold is absolute or relative.
Decide on a sliding window approach using a queue (or deque) to store the last N temperatures, and maintain a running sum to compute the average in O(1) time per reading.
Iterate through the readings, update the window and sum, compute the rolling average, and compare the current temperature to the average plus threshold. If it exceeds, record the spike.
State that the time complexity is O(M) for M readings and space complexity is O(N) for the window. Discuss potential optimizations like using a circular buffer or handling streaming data.
Walk through a small example, test with N=1, threshold=0, and cases where spikes occur at the beginning or when there are fewer than N readings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.