Number of Music Playlists
A hard-tier problem at 60% community acceptance, tagged with Math, Dynamic Programming, Combinatorics. Reported in interviews at Coursera and 0 others.
You've got N songs and you need to build a playlist of exactly K songs where every song appears at least once. The trick is that songs can repeat, and the order matters. This is a Coursera interview problem that catches people off guard because it looks like a permutation at first, then you realize the math is combinatorial. If you hit this during your OA and blank on the recurrence, StealthCoder surfaces the working solution in seconds while the proctor watches something else.
Companies that ask "Number of Music Playlists"
Number of Music Playlists 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 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 StealthCoderThe problem lives at the intersection of Math and Dynamic Programming, which is where most candidates choke. The naive approach is to think "pick K songs from N, then order them," but that's wrong because you're forced to use each song at least once and songs repeat. The real solution uses DP where you track how many distinct songs you've used so far, then count the ways to fill the remaining slots. The trick: at each step, you either repeat a song you've already used or introduce a new one. This creates a recurrence that isn't obvious without seeing the pattern. Combinatorics handles the counting part. Most people either try brute force permutations or give up on the counting. StealthCoder is the hedge for this specific problem when the pattern hasn't clicked during prep.
Pattern tags
You know the problem.
Make sure you actually pass it.
Number of Music Playlists 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. 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.
Number of Music Playlists interview FAQ
Is this really asked at Coursera?+
Yes, it's in their reported list. It's a hard problem, so expect it only in senior roles or strong candidate signals. The low company count means it's niche, not a staple like LRU Cache, but Coursera takes it seriously.
What's the trick I'm missing?+
Stop thinking permutations. This is about DP state: dp[i][j] = ways to build a playlist of length i using exactly j distinct songs. At each step, either pick a song you've used (j choices) or introduce a new one (N-j choices). The recurrence is the whole game.
How does this relate to combinatorics?+
Combinatorics gives you the counting formula. You're essentially distributing K-N identical items (extra plays) into N songs. That's a stars-and-bars problem wrapped inside the DP. Without understanding combinatorics, the answer looks magical.
Do I need to memorize a formula?+
No. Build the DP table and let recurrence do the work. Memorizing formulas for this type of problem fails under pressure. Understand the state transition, write clean loops, and let math follow.
Is 60% acceptance rate a real difficulty level?+
It's labeled hard for a reason. 60% means people who attempt it have some prep or strong math. If you're seeing it cold, don't expect to solve it in 15 minutes. Drill the DP pattern or know when to deploy your safety net.
Want the actual problem statement? View "Number of Music Playlists" on LeetCode →