Take Gifts From the Richest Pile
A easy-tier problem at 76% community acceptance, tagged with Array, Heap (Priority Queue), Simulation. Reported in interviews at DE Shaw and 0 others.
You're given piles of gifts with different values, and you must take from the richest pile exactly n times. This problem shows up in DE Shaw assessments and looks deceptively simple on first read. The trap is assuming you can brute-force it. You can't iterate through all piles n times if n is large and you have thousands of piles. The trick is a max heap. Most candidates who've prepped heap basics nail this in under five minutes. If you blank on heap syntax during the live OA, StealthCoder surfaces a working solution invisibly while you regain focus.
Companies that ask "Take Gifts From the Richest Pile"
Take Gifts From the Richest Pile 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 because the OA filter rejects engineers who'd pass the on-site. That's a broken filter. This is the workaround.
Get StealthCoderThe naive approach sounds safe: loop n times, find the max pile, take one gift, repeat. That's O(n * m) where m is pile count, and it times out. The real solution maintains a max heap of pile values. Each operation extracts the max, decrements it, and reinserts. This is O(n log m), which handles large n without breaking a sweat. The gotcha is heap implementation varies by language. Python's heapq is a min heap by default, so you negate values. Java's PriorityQueue works fine as a max heap with a custom comparator. C++ priority_queue defaults to max. This is one of those problems where the pattern is simple once you see it, but forgetting heap mechanics under pressure is common. StealthCoder is the safety net for the moment you remember it uses a heap but can't recall your language's exact syntax or how to structure the loop.
Pattern tags
You know the problem.
Make sure you actually pass it.
Take Gifts From the Richest Pile 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 because the OA filter rejects engineers who'd pass the on-site. That's a broken filter. This is the workaround. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Take Gifts From the Richest Pile interview FAQ
Is this problem actually easy?+
The acceptance rate is 75.6%, which confirms it. The conceptual leap to heaps is small, and the implementation is straightforward once you commit to the pattern. The problem tests whether you reflexively reach for heap instead of naive iteration, not whether you can invent complex logic.
Do I need to optimize for this, or will brute-force pass?+
Depends on the constraints in your specific OA, but brute-force loops are risky at scale. A heap solution guarantees you won't timeout on large n values. DE Shaw pays attention to algorithmic efficiency, so heap is the expected approach here.
What's the trick to getting it right on the first try?+
Recognize immediately that you're repeatedly finding a maximum. That's a heap signal. Write out the heap operations before coding: extract max, decrement, reinsert. Then translate to your language's specific syntax. Most mistakes come from mixing up min and max heap semantics.
How does this relate to other heap problems I should know?+
This is the simplest form of heap manipulation. Once you're comfortable with extract-max and reinsert, you're ready for harder problems like reorganizing strings or rearranging tasks. It's foundational, not complex.
Why does DE Shaw ask this?+
It tests whether you pick the right data structure under time pressure. It's not about algorithmic genius, it's about solid fundamentals and reflex. That matters for finance-engineering roles where efficiency and correctness are non-negotiable.
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