Maximum Product of Two Elements in an Array
A easy-tier problem at 83% community acceptance, tagged with Array, Sorting, Heap (Priority Queue). Reported in interviews at Samsung and 2 others.
Maximum Product of Two Elements in an Array is an easy problem that shows up on Samsung, J.P. Morgan, and Cisco assessments. At first it feels trivial: pick two numbers, multiply them, return the biggest result. But the catch is the constraint that separates the one-liner from the real solution. With an 83% acceptance rate, most candidates pass, but the ones who miss the edge case during their live OA are the ones who didn't read the problem statement twice. If you blank on why the obvious greedy approach works here, StealthCoder surfaces the correct logic in seconds, invisible to the proctor.
Companies that ask "Maximum Product of Two Elements in an Array"
Maximum Product of Two Elements in an Array 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 trick is understanding that to maximize the product of two distinct array elements, you need the two largest numbers. Sorting gets you there, or a single pass to track max and second max. The catch is the constraint that often trips people: the problem specifies nums[i] - 1, meaning all numbers are positive integers at least 1. This is critical. If negatives were allowed, you'd have to consider the product of two negatives. But here, the greedy 'grab the two biggest' is bulletproof. Common failure: scanning left to right and stopping early, or overthinking and implementing a heap when sorting is cleaner. If you hit this problem live and freeze on whether negatives matter, StealthCoder has your back with a clean, passing solution.
Pattern tags
You know the problem.
Make sure you actually pass it.
Maximum Product of Two Elements in an Array recycles across companies for a reason. It's easy-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.
Maximum Product of Two Elements in an Array interview FAQ
Why isn't this problem just 'sort and return product of last two'?+
It is. That's the intended solution. The only gotcha is reading the constraint carefully: positive integers only (nums[i] >= 1 per problem definition) means negatives can't trick you. Sorting is O(n log n), a single pass to track top two is O(n) and just as clean to code under pressure.
Do Samsung, J.P. Morgan, and Cisco ask this differently each time?+
Unlikely. The problem structure is stable across reports. What changes is your time to code it correctly. At this difficulty level, the main risk is rushing and skipping the constraint read, not algorithmic variation between companies.
Is this a real screening question or warm-up?+
Warm-up. An easy problem with 83% acceptance rate signals a confidence check or first-round filter. If it appears in your OA, you're expected to nail it quickly and move on to harder problems. Missing it costs time and morale.
Should I use a heap for this?+
No. A heap (priority queue) is overkill. Sorting takes the same complexity in practice for small arrays and is clearer to write. A single pass to track max and second-max is fastest (O(n) time, O(1) space), but sorting is safer under stress if you code it correctly.
What's the real lesson for harder problems?+
This problem teaches you to read constraints first. The constraint (positive integers) changes everything about approach. On harder problems, missing one constraint detail can lead you down a wrong algorithmic path entirely. Treat constraint-reading as part of the solution.
Want the actual problem statement? View "Maximum Product of Two Elements in an Array" on LeetCode →