Count Subarrays With Fixed Bounds
A hard-tier problem at 69% community acceptance, tagged with Array, Queue, Sliding Window. Reported in interviews at MathWorks and 5 others.
Count Subarrays With Fixed Bounds is a hard problem that appears in assessments for Uber, Morgan Stanley, Snowflake, and other tier-one companies. The premise is deceptively simple: count subarrays where every element falls within a given range [minK, maxK]. But the naive approach times out. This isn't about brute force iteration. It's about recognizing a sliding window pattern and using positional tracking to count valid subarrays in linear time. With a 69% acceptance rate, most candidates who see this problem don't solve it cleanly. If you hit it live and blank on the trick, StealthCoder surfaces a working solution instantly, invisible to the proctor.
Companies that ask "Count Subarrays With Fixed Bounds"
Count Subarrays With Fixed Bounds 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. Made by an Amazon engineer who watched the leaked-problem repo become an industry secret. He decided you should have it too.
Get StealthCoderThe trap is thinking you need to check every subarray explicitly. Instead, the key insight is: count subarrays that include at least one element outside [minK, maxK], then subtract from the total. More precisely, use a sliding window to track the rightmost position where you've seen both minK and maxK, and the rightmost position of any element outside the bounds. For each right boundary, the number of valid subarrays ending there is determined by how far left you can go before hitting an out-of-bounds element. This uses Array traversal with Sliding Window logic and sometimes a Monotonic Queue to optimize. The algorithm runs in O(n) time, which is mandatory at hard difficulty. Most candidates attempt nested loops or Queue implementations without understanding the positional arithmetic. StealthCoder hedges the moment you realize the greedy window approach isn't obvious.
Pattern tags
You know the problem.
Make sure you actually pass it.
Count Subarrays With Fixed Bounds recycles across companies for a reason. It's hard-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. Made by an Amazon engineer who watched the leaked-problem repo become an industry secret. He decided you should have it too. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Count Subarrays With Fixed Bounds interview FAQ
Is this really asked at Uber and Morgan Stanley?+
Yes. It appears in the reported interview history of both. It's a favorite for screening engineers who understand sliding window beyond the textbook. The 69% acceptance rate suggests it's solvable in an hour if you know the pattern, brutal if you don't.
What's the trick I'm missing if my loop times out?+
You're checking subarrays explicitly instead of using position math. The key: don't iterate all subarrays. Track the rightmost index of a violation and the rightmost index where you last saw both bounds. Subtract, don't count.
Do I need a Monotonic Queue for this?+
Not necessarily. Monotonic Queue appears in the topic list because some solutions use it for edge cases, but a clean sliding window with two-pointer tracking and positional arithmetic solves it without one. Know both approaches if you have time.
How does this differ from other sliding window problems?+
Most sliding window problems shrink or expand based on a condition. This one uses positional history instead. You're not dynamically adjusting the window; you're counting how many valid windows end at each position based on past violations.
Will I see this if I'm interviewing at a smaller company?+
Lower probability than at Uber or Snowflake, but Meesho and OKX have also asked it. At smaller shops, hard Array problems are less common but not rare. If you're targeting tier-one, this is a must-drill.
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