Wish coding interview
questions, leaked.
4 problems reported across recent Wish interviews. Top patterns: database, array, greedy. The list below is what most reported candidates actually saw, plus the honest play if you can't grind all of it.
Wish's assessment is lean and brutal. Four problems, one hard, and the difficulty curve climbs fast. You're looking at database work up front (two of the four), then pattern-matching on greedy and array problems that demand both speed and precision. The median candidate blanks on database syntax or greedy state-tracking under time pressure. StealthCoder runs invisibly during your assessment and solves whatever you can't build in the moment. Know your weakest topic before you sit down, because the problem set doesn't repeat.
Top problems at Wish
| # | Problem | Diff | Frequency | Pass % | Patterns |
|---|---|---|---|---|---|
| 01 | Monthly Transactions II | MEDIUM | 100.0 | 42% | Database |
| 02 | Monthly Transactions I | MEDIUM | 100.0 | 58% | Database |
| 03 | Rabbits in Forest | MEDIUM | 100.0 | 58% | Array · Hash Table · Math |
| 04 | Find Servers That Handled Most Number of Requests | HARD | 100.0 | 44% | Array · Greedy · Heap (Priority Queue) |
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 Wish OA. The proctor cannot see it. Screen share cannot detect it. Built by an Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code.
Get StealthCoder- database2 · 50%
- array2 · 50%
- greedy2 · 50%
- hash table1 · 25%
- math1 · 25%
- heap priority queue1 · 25%
- ordered set1 · 25%
Database dominates the first half of this assessment. Two problems back-to-back means you can't skip SQL fundamentals. Array and greedy tie at two appearances each, and they're intertwined. The hard problem stacks array, greedy, heap, and ordered-set logic into one question. That's where most candidates crater. You need fast array iteration, greedy state selection (which servers to assign), and efficient data structure picks (heap for min-cost, ordered-set for availability). Hash table and math each show once but integrate into the greedy problem. Study database first to clear two problems cleanly, then drill greedy with a focus on the hard problem's multi-layer state management. StealthCoder covers your blind spots on the live OA when the clock runs down.
Companies with similar patterns
If you prepped for Wish, these companies recycle ~60% of the same topics.
You've seen the list.
Now make sure you pass Wish.
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 by an Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Wish interview FAQ
How much SQL should I know for Wish's assessment?+
Two of four problems are pure database. You need to write window functions, aggregations, and joins fluently. Focus on transaction-level problems (Monthly Transactions I and II use real SQL patterns). You can't fake this one. Drill CTEs, CASE, and GROUP BY until it's muscle memory.
Is the hard problem harder because of the heap, or the greedy logic?+
Both. Find Servers That Handled Most Requests requires you to track availability with a heap, assign greedily to the earliest free server, and scan an ordered-set efficiently. If you know greedy selection but not heaps, you'll timeout. Study the hard problem's state machine first, then implement it.
Should I drill array problems before greedy ones?+
No. Array and greedy are tied in frequency, but greedy is harder. Start with Rabbits in Forest (medium greedy with array). It teaches you how to layer greedy decisions over array iteration. Then move to the hard problem. Arrays alone won't help here.
What if I haven't used ordered-sets before?+
The hard problem uses ordered-set for efficient queries. In Python, use sortedcontainers.SortedList or a balanced BST. In Java or C++, TreeMap or std::set. The concept is simple: maintain a sorted collection and query/remove in log time. One afternoon of practice is enough.
Can I skip hash-table since it only appears once?+
It appears in one problem but as a secondary tool, not the main challenge. You'll use it for counting and lookups in Rabbits in Forest. Don't skip it, but it's not a bottleneck. Database and greedy are your real gatekeepers.