Count Almost Equal Pairs I
A medium-tier problem at 38% community acceptance, tagged with Array, Hash Table, Sorting. Reported in interviews at Info Edge and 0 others.
Count Almost Equal Pairs I is a medium-difficulty problem with a 37.5% acceptance rate, asked by Info Edge. You're given an array and need to count pairs that satisfy a specific "almost equal" condition, which sounds simple until you realize the naive O(n²) check doesn't scale and the condition itself is easy to misread. This is the kind of problem where candidates blank on the pattern during a live assessment. If it lands in your OA and you hit a wall, StealthCoder solves it invisibly in seconds.
Companies that ask "Count Almost Equal Pairs I"
Count Almost Equal Pairs I 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 a senior engineer who knows the OA is theater. This is the script.
Get StealthCoderThe trick is recognizing what "almost equal" actually means for the problem's specific definition, then choosing between brute force, hash table optimization, or sorting. Most candidates attempt O(n²) enumeration first, which works for small inputs but fails on larger test cases. The hash table approach can reduce complexity if you can precompute or group numbers by a derived key. Sorting opens up two-pointer or binary-search paths depending on the exact constraint. The gap between "almost" working and actually passing is the difference between understanding the condition precisely and implementing a scalable structure. StealthCoder reads the problem statement on your screen, identifies the condition, and delivers the optimal approach before you've even sketched a second solution.
Pattern tags
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Count Almost Equal Pairs I 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 a senior engineer who knows the OA is theater. This is the script. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Count Almost Equal Pairs I interview FAQ
What's the actual trick to 'almost equal' here?+
Almost equal is defined by the problem's specific numeric condition, not a range. Read the constraint carefully: it's about digit differences, bit manipulation, or value offset, not simple subtraction. Misreading costs you half the test case results. Verify with the examples first.
Is brute force O(n²) fast enough?+
For small arrays (n < 1000), yes. Beyond that, it times out. Hash table or sorting brings it to O(n log n) or O(n). The problem doesn't cap array size, so expect medium inputs where optimization matters.
Is this still asked at tech companies?+
Info Edge reports it. Pair-counting problems appear regularly in interviews because they test enumeration strategy, data structure choice, and time-complexity awareness. It's not hyper-common but realistic.
How does this relate to the other array and hash table topics?+
Array enumeration is the baseline. Hash table speeds up lookups. Sorting opens two-pointer approaches. You'll likely pick one primary technique and discard the others. Understanding which one fits the constraint is the real skill.
How much time should I spend on this in a real OA?+
Read and understand the condition fully (3 minutes). Code and test brute force (5 minutes). If it passes, move on. If it times out, pivot to hash table or sorting (7 minutes more). Don't get stuck past 20 minutes.
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