Patching Array
A hard-tier problem at 54% community acceptance, tagged with Array, Greedy. Reported in interviews at Snowflake and 1 others.
Patching Array is a hard-rated greedy problem that Snowflake and Flipkart both ask. The acceptance rate sits just above 50%, which means roughly half the people who attempt it walk away empty-handed. You're given an array and need to figure out which minimum patches turn a gapped range into a complete one. The trick isn't about sorting or searching. It's about knowing when to stop iterating and when to jump ahead. If you blank on the pattern during your live assessment, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
Companies that ask "Patching Array"
Patching 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. 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 insight is greedy: track the furthest range you can currently patch, then always pick the patch that extends that range the most. Most candidates start by trying to patch everything they see, which burns through patches fast and fails test cases. The real pattern is understanding that you don't care about individual elements; you care about intervals. Once you hit an unpatchable gap, you must patch. Choose the patch that gives you the largest jump forward. This is where intuition breaks down. The greedy choice isn't obvious until you see it once. That's precisely the moment StealthCoder saves you during an OA. Array and Greedy work together here because you iterate once, make greedy decisions, and move on.
Pattern tags
You know the problem.
Make sure you actually pass it.
Patching Array 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.
Patching Array interview FAQ
What makes this greedy problem different from other array problems?+
Most array problems ask you to find a value or rearrange elements. Patching Array asks you to find the minimum operations to hit a goal state. The greedy choice is not 'pick the biggest value,' it's 'pick the patch that extends your reachable range furthest.' That inversion catches many candidates.
Is Patching Array still asked at Snowflake and Flipkart?+
Both companies have reportedly asked it. It's a 50-50 acceptance rate problem, so prepare for the possibility. Hard problems at scale companies often test your ability to spot the trick quickly, not grind code.
What's the most common mistake candidates make?+
Trying to patch every gap or iterating through all elements greedily without tracking intervals. The moment you think 'I'll patch this number,' you've already lost. Think 'range' instead, and the problem clicks.
How does greedy strategy apply here versus other array problems?+
In most greedy array problems, you pick the locally optimal choice at each step (e.g., earliest deadline). Here, you pick the patch that maximizes your future reach. It's greedy on outcome, not on the element itself.
Should I practice this problem before my live assessment?+
If Patching Array appears in your company target list, yes. If it doesn't, understand the range-tracking pattern anyway. Hard greedy problems test pattern recognition, and that pattern shows up across platforms.
Want the actual problem statement? View "Patching Array" on LeetCode →