Memoize
A medium-tier problem at 64% community acceptance, tagged with . Reported in interviews at Shopee and 1 others.
Memoization is the silent killer in coding interviews. You nail the logic, but the interviewer asks 'can we optimize this' and you're stuck. This problem forces you to implement caching from scratch instead of just knowing the concept. It's asked at Microsoft and Shopee with a 64% acceptance rate, which means most candidates either get it quick or tank it hard. If you can't implement memoization cleanly under pressure, StealthCoder solves it invisibly during your live assessment so you don't blow the solve.
Companies that ask "Memoize"
Memoize 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 trick here isn't the algorithm itself. It's understanding that memoization is a disciplined way to cache results so you don't recompute the same inputs twice. Most candidates understand the idea but botch the implementation: they forget to check the cache before computing, or they cache at the wrong scope, or they use the wrong data structure for the key. The problem tests whether you can write a decorator or wrapper that actually works, not just recite the definition. When you hit this live and the obvious recursive solution times out, you need to add memoization without rewriting everything. That's where most candidates panic. StealthCoder reads the exact problem structure and shows you the correct caching pattern in seconds, keeping your solution on track.
You know the problem.
Make sure you actually pass it.
Memoize recycles across companies for a reason. It's medium-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.
Memoize interview FAQ
Is memoization still asked at big tech companies?+
Yes. Microsoft and Shopee both ask it, and the 64% acceptance rate suggests it's a filter problem. Companies test memoization because it separates candidates who can optimize recursion from those who can't. If you haven't drilled it, expect it to appear in your OA.
What's the difference between memoization and dynamic programming?+
Memoization caches results of recursive calls top-down. DP is bottom-up table-building. This problem focuses on memoization specifically. You're adding a cache layer to an existing function, not rewriting it as DP. The pattern matters for the implementation test.
How do I know if my memoization is correct?+
Test it with a function that has overlapping subproblems. Fibonacci is the classic. If the same input is called twice, the second call should hit the cache, not recompute. If your solution still times out or makes duplicate calls, your cache is broken.
What's the most common memoization mistake?+
Forgetting to check the cache before computing, or using unhashable types as keys. In Python, lists can't be dictionary keys. You'll convert them to tuples or use frozensets. One missed hashability check and your solution fails on edge cases.
Does this problem require a specific language?+
No language restrictions are listed. Memoization patterns exist in Python, JavaScript, Java, and C++. The core concept is language-agnostic, but the implementation details vary. Pick a language you're strong in and focus on the caching logic, not syntax.
Want the actual problem statement? View "Memoize" on LeetCode →