Maximum Spending After Buying Items
A hard-tier problem at 60% community acceptance, tagged with Array, Greedy, Sorting. Reported in interviews at Zomato and 1 others.
Maximum Spending After Buying Items is a hard problem that Zomato and TikTok have both asked in their hiring processes. It sits at a 60% acceptance rate, which means the trick isn't immediately obvious, even though the problem statement sounds straightforward. You're dealing with an array or matrix of item prices, and you need to figure out the optimal order to buy them to maximize remaining spending. The greedy intuition that feels right will cost you. This is exactly the kind of problem where you nail the obvious approach, watch it fail on test case 8, and run out of time reworking it live. StealthCoder surfaces the correct pattern in seconds if you hit this in your OA.
Companies that ask "Maximum Spending After Buying Items"
Maximum Spending After Buying Items 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 a working Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE.
Get StealthCoderThe core trap is thinking you should buy cheap items first to preserve money for expensive ones. That's backwards. The real constraint is that each purchase operation costs more than the last one. If you buy item i at step j, you pay price[i] * (j + 1). The greedy win is buying the most expensive items earliest, when the multiplier is lowest. Sort descending, buy the heaviest prices when cost is cheapest, and sum up. Most candidates invert the sort order or miss the multiplier cost structure entirely. The problem combines Sorting, Greedy reasoning, and sometimes Heap manipulation if you're working with a matrix of item prices across multiple categories. Array manipulation and problem modeling are also critical. If this appears in your live assessment and the greedy pattern doesn't click under time pressure, StealthCoder handles the sort and cost calculation in real time, invisible to the proctor.
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Maximum Spending After Buying Items 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 a working Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Spending After Buying Items interview FAQ
Why does buying expensive items first actually maximize spending?+
Each purchase has a cost multiplier that increases with every step. Buying expensive items when the multiplier is low (early) costs less total money than buying them late. Reverse-sort prices and multiply each by its step number plus one. The math works out so you minimize total cost.
Is this problem still asked at TikTok and Zomato?+
Yes. Both companies have recently included it in their hiring pipeline. At 60% acceptance, it's challenging enough to separate candidates who can spot the greedy pattern from those who get stuck on the intuitive but wrong approach.
What's the trick to not falling for the obvious wrong answer?+
The obvious wrong answer is sorting ascending and buying cheap items first. That preserves money but ignores the multiplier cost. Invert your thinking: the multiplier is your constraint, not the item price alone. Expensive items bought early pay off.
Does sorting solve it, or do I need a heap?+
Sorting is your main tool. Greedy sorting on the price array in descending order gives you the answer. A heap or priority queue might appear if you're managing prices across multiple categories in a matrix format, but the core trick is the sort and the multiplier math.
How hard is this compared to other greedy problems?+
It's solidly hard because the greedy choice is counterintuitive. Most people's first instinct fails. The acceptance rate of 60% reflects that many smart candidates get trapped by the wrong greedy intuition before finding the right one.
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