Licious coding interview
questions, leaked.
2 problems reported across recent Licious interviews. Top patterns: array, hash table, binary search. The list below is what most reported candidates actually saw, plus the honest play if you can't grind all of it.
Licious interviews are tight. Two problems dominate their assessment, and you're looking at one medium and one hard. First Missing Positive is a brutal hash-table and array problem that trips up candidates who haven't internalized the O(n) space trick. Longest Increasing Subsequence requires you to know the binary-search optimization cold. If you blank mid-assessment, StealthCoder runs invisibly and surfaces a working solution in seconds so you don't tank the round.
Top problems at Licious
| # | Problem | Diff | Frequency | Pass % | Patterns |
|---|---|---|---|---|---|
| 01 | First Missing Positive | HARD | 100.0 | 41% | Array · Hash Table |
| 02 | Longest Increasing Subsequence | MEDIUM | 100.0 | 58% | Array · Binary Search · Dynamic Programming |
Frequencies derived from public community-tagged interview reports. Click a row to view on LeetCode.
You have a week, maybe less. You can't out-grind the list above. StealthCoder runs invisibly during the actual Licious OA. The proctor cannot see it. Screen share cannot detect it. Built because the OA filter rejects engineers who'd pass the on-site. That's a broken filter. This is the workaround.
Get StealthCoder- array2 · 100%
- hash table1 · 50%
- binary search1 · 50%
- dynamic programming1 · 50%
Arrays show up in both problems, which means your first week should be array manipulation, especially in-place operations and boundary edge cases. Hash tables appear once but in the harder problem, so treat that as a forced topic. Binary search and dynamic programming each appear once, but they're bundled into the medium problem as a combo pattern. That's your second week focus. StealthCoder is your hedge on whichever pattern you didn't have time to drill deep. The gap between medium and hard is steep here, so expect the hard problem to separate strong candidates from the rest.
Companies with similar patterns
If you prepped for Licious, these companies recycle ~60% of the same topics.
You've seen the list.
Now make sure you pass Licious.
Memorizing every problem above in a week is a fantasy. StealthCoder is the hedge: an AI overlay that's invisible during screen share. It reads the problem on screen 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.
Licious interview FAQ
Should I solve both Licious problems before my assessment?+
Yes, but that's not enough. These two problems teach you the patterns Licious values: array tricks and dynamic optimization. Solve them, understand the approach, then drill 5-10 similar array and DP problems so the pattern recognition is automatic during the real assessment.
How many array problems should I do if I'm weak on arrays?+
Since arrays appear in both of Licious's top problems, prioritize them. Target 15-20 array problems before your assessment, especially in-place manipulation and two-pointer techniques. First Missing Positive itself is a good starting point.
Is binary search important for Licious, or should I focus on hash tables first?+
Hash tables appear in one hard problem, binary search in one medium. But binary search pairs with DP in the medium problem, making it a combo skill check. Study hash tables first for the hard problem, then move to binary search with DP for the medium.
What's the hardest part of Licious's assessment?+
First Missing Positive requires you to think in-place with O(1) space while using array indices as a hash. Most candidates default to a hash table, which works but isn't optimal. The hard problem is where Licious separates candidates, so drill the index-as-hash trick until it's muscle memory.
Do I need to know dynamic programming well for Licious?+
Yes. Longest Increasing Subsequence is a classic DP problem, and Licious uses it to test if you can optimize with binary search. Know the O(n log n) solution, not just the O(n squared) DP approach. This problem bundles three topics, so it's a pattern-recognition filter.