Number of Sub-arrays of Size K and Average Greater than or Equal to Threshold
A medium-tier problem at 70% community acceptance, tagged with Array, Sliding Window. Reported in interviews at Turo and 2 others.
You're working through a medium-difficulty array problem that tests sliding window pattern recognition. Turo, Goldman Sachs, and LinkedIn have all asked this one. The twist: you're not just finding a single subarray, you're counting all contiguous subarrays of size K where the average meets or exceeds a threshold. Most candidates either iterate poorly or re-compute the sum from scratch each time. The acceptance rate sits around 70%, which means plenty of people are missing the efficiency play. If you hit this during a live OA and freeze on optimization, StealthCoder solves it invisibly in seconds.
Companies that ask "Number of Sub-arrays of Size K and Average Greater than or Equal to Threshold"
Number of Sub-arrays of Size K and Average Greater than or Equal to Threshold is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built by an engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share.
Get StealthCoderThe sliding window pattern is your friend here, but you need to think in sums, not averages. Instead of dividing each window's sum by K to check the average, multiply the threshold by K and compare sums directly. Avoid floating-point errors and shave cycles off. Build your first window by summing the first K elements. Then slide right: subtract the leftmost element, add the new rightmost element, check the condition, and count. The trap most candidates fall into is recomputing the entire window sum on each iteration, which tanks you from O(n) to O(n*K). Lock in the sliding window correctly and you're done. This is a pattern recognition problem hiding inside an array problem. If the sliding window doesn't click before your OA, StealthCoder is the safety net that surfaces the optimal solution when you blank.
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Number of Sub-arrays of Size K and Average Greater than or Equal to Threshold recycles across companies for a reason. It's medium-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built by an engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Number of Sub-arrays of Size K and Average Greater than or Equal to Threshold interview FAQ
Is this problem actually hard, or is 70% acceptance because it's straightforward?+
It's a pattern-recognition gate. The core logic is simple once you see sliding window, but many candidates either don't recognize the pattern or implement it inefficiently. The 70% acceptance suggests plenty of people get it right, but the ones who don't often fail on optimization, not correctness.
Why do I need to multiply threshold by K instead of dividing the sum?+
Floating-point division can introduce rounding errors. By rearranging the inequality (sum >= threshold * K), you compare integers and sidestep precision issues. It's faster and cleaner.
Does LinkedIn or Goldman Sachs weight this differently in their loop?+
Unknown from reporting data. Both companies ask sliding window problems frequently, so they're testing your ability to spot the pattern and implement it correctly. Treat it the same regardless of source.
What's the most common mistake after you realize it's sliding window?+
Forgetting to initialize the first window before entering the main loop, or re-summing the entire window instead of sliding it. Both burn time and introduce bugs. Initialize once, then slide.
How does this relate to other sliding window problems I should know?+
It's a basic template. Once you lock this pattern, you can handle constraints on max/min in windows, longest substrings with conditions, and similar problems. The core remains: compute first window, then slide and update.
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