Minimum Operations to Collect Elements
A easy-tier problem at 61% community acceptance, tagged with Array, Hash Table, Bit Manipulation. Reported in interviews at Deutsche Bank and 0 others.
Minimum Operations to Collect Elements is an easy problem with a 61% acceptance rate, asked by Deutsche Bank. It's a filtering problem that looks simple on the surface but trips up candidates who overthink the collection logic. The pattern involves recognizing when you've gathered a specific set of values and can stop early. Most people get the core idea right but waste time on unnecessary data structures or miss the stopping condition entirely. If this hits your live assessment and you blank on the optimal approach, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
Companies that ask "Minimum Operations to Collect Elements"
Minimum Operations to Collect Elements 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 trick is understanding that you're not sorting or fully processing the array. You need to track which target elements you've seen, then return the index where you've finally collected them all. Hash tables let you check membership in constant time, and bit manipulation can encode which elements you've found in a compact way. The common mistake is iterating the entire array when you should stop as soon as you hit your target count. Array iteration plus a simple lookup hits the problem's constraints easily. The real edge case is recognizing that the order of collection matters, not the value itself. StealthCoder is the safety net if you get stuck on whether to use a set, a bitmask, or a counter variant.
Pattern tags
You know the problem.
Make sure you actually pass it.
Minimum Operations to Collect Elements 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. 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.
Minimum Operations to Collect Elements interview FAQ
Is this problem actually easy or is the acceptance rate misleading?+
The 61% acceptance rate reflects that it's genuinely easy but candidates often overcomplicate it. The core logic is straightforward: iterate, track seen elements, stop when done. Most rejections come from off-by-one errors or missing the early stopping condition, not algorithmic confusion.
Should I use a hash table, a set, or bit manipulation?+
Hash tables and sets are both fast and clear. Bit manipulation is clever if you know the element range is small, but it's overkill here. A set is the safest choice: fast, readable, and sufficient. Don't optimize prematurely.
What's the stopping condition that catches most candidates?+
Many people iterate the whole array instead of returning as soon as they've collected all target elements. The problem rewards early exit. Track your count and return the index the moment you hit your goal, not at the end.
Does Deutsche Bank ask this in a real interview or just in OAs?+
The data shows Deutsche Bank has asked it, but we don't have details on the interview stage or loop context. Treat it as a possible warm-up or screening problem, not a hard round question.
How does this relate to other array and hash table problems?+
It's a foundational pattern: iterate once, track state with a hash table, stop early when a condition is met. You'll see this structure in sliding-window, two-pointer, and target-sum variants across many companies.
Want the actual problem statement? View "Minimum Operations to Collect Elements" on LeetCode →