Reservoir Sampling interview questions
3 reservoir sampling problems tagged across recent interview reports. Drilled most heavily by meta, nvidia, and goldman sachs.
Reservoir Sampling solves a narrow but high-value problem: pick a uniformly random element from a stream of unknown length without loading the entire stream into memory. It's a low-frequency pattern, only 3 known problems, but Meta and Nvidia ask it, and it shows up in live OAs as a surprise blocker. The algorithm is simple once you see it, lethal if you don't. StealthCoder catches the variant you didn't drill and gives you the answer in seconds, invisible to the proctor.
Most-asked reservoir sampling problems
| # | Problem | Diff | # Companies | Pass % |
|---|---|---|---|---|
| 01 | Linked List Random Node | MEDIUM | 1 | 64% |
| 02 | Random Flip Matrix | MEDIUM | 1 | 44% |
| 03 | Random Pick Index | MEDIUM | 1 | 65% |
You can't drill every reservoir sampling variant before the assessment. StealthCoder runs invisibly during screen share and solves whichever variant they throw at you. No browser extension. No detection signature. 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 StealthCoderReservoir Sampling appears in two flavors: sampling a single random element (Linked List Random Node, Random Pick Index) and sampling from a 2D structure (Random Flip Matrix). The pattern is recognizable by the constraint: you either don't know the stream length upfront or can't afford to store it. The core insight is Knuth's algorithm, maintain a reservoir of size k, then for the i-th element (i > k), include it with probability k/i. Most candidates freeze on this one because it's not a tree or graph problem; it feels alien. If a Reservoir Sampling variant lands in your live OA cold, StealthCoder solves it in seconds and you move on.
Companies that hire most on reservoir sampling
3 reservoir sampling problems.
You won't drill them all. Pass anyway.
Reservoir Sampling is one of the patterns interviews actually filter on. Memorizing every variant in a week is a fantasy. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds, no matter which reservoir sampling flavor lands in your live OA. 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.
Reservoir Sampling interview FAQ
How many Reservoir Sampling problems should I drill before an interview?+
With only 3 known problems, drill all three: Linked List Random Node, Random Flip Matrix, and Random Pick Index. You'll recognize the pattern and the algorithm immediately. Most interviews won't ask a fourth variant.
Does Meta or Nvidia drill Reservoir Sampling harder?+
Meta and Nvidia both have 2 problems each on this pattern. Neither is an obsessive repeater, but it's a known signal. Goldman Sachs also has 1. It's a rare, high-signal pattern, worth studying because you'll stand out if you nail it.
How do I recognize a Reservoir Sampling problem in the wild?+
Look for: unknown or very large stream length, need to pick k random elements without loading all data, or uniformity requirement. If you can't iterate twice or store the whole input, Reservoir Sampling is probably the move.
Is Reservoir Sampling harder than other algorithmic patterns?+
No, it's short and elegant once you know it. The hard part is recognizing it. Most candidates have never heard of Knuth's algorithm. That's the gap StealthCoder bridges in a live OA.
What's the time and space complexity I need to know?+
O(n) time (one pass through the stream) and O(k) space (reservoir size). This efficiency is why the pattern exists. If you're storing the whole input, you've missed the constraint.