Movie Ratings
Reported by candidates from Akuna's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
Akuna's August OA included a problem called Movie Ratings with no obvious algorithmic pattern telegraphed in the title alone. This is a signal: the real trick isn't the algorithm, it's parsing what the problem is actually asking. You'll have a few minutes to read the full problem statement during the OA. StealthCoder can read that statement and surface the pattern instantly if you blank on approach. The problem likely tests your ability to handle data aggregation, filtering, or ranking. Get the problem text right and the solution becomes obvious.
Pattern and pitfall
Without the full problem statement, the most common Movie Ratings variants test hash tables (to count/group ratings) or sorting (to rank movies by aggregate score or frequency). Some versions ask you to filter by threshold, others to return top-K movies. The common trap is misunderstanding the sorting criteria: do you sort by average rating, count of ratings, or both. Read the output format carefully. If you've built the hash table and sorted correctly, you're 95 percent done. StealthCoder will catch misreads of the problem statement in real time during the OA, so if you second-guess yourself under pressure, you have a safety net.
The honest play: practice the pattern, and have StealthCoder ready for the one you didn't see coming.
You can drill Movie Ratings cold, or you can hedge it. StealthCoder runs invisibly during screen share and surfaces a working solution in under 2 seconds. The proctor sees the IDE. They don't see what's behind it. Built for the candidate who saw this exact problem leak two days before his OA and wondered if anyone had a play.
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Akuna reuses patterns across OAs. Built for the candidate who saw this exact problem leak two days before his OA and wondered if anyone had a play. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Movie Ratings FAQ
Is this a hash table problem?+
Very likely. Movie Ratings almost always involves grouping ratings by movie and aggregating them. The hash table stores counts or sums. The hard part isn't the data structure, it's understanding what metric you're sorting by. Read the output requirements twice.
What's the most common pitfall?+
Misreading the sorting criteria. Candidates often assume 'highest rated' means average score, but the problem might ask for most-rated (count), or a tie-break rule. Skim the output format before coding.
Do I need to handle edge cases like tied ratings?+
Yes. If two movies have the same aggregate score, the problem usually specifies a secondary sort order (alphabetical, insertion order, or rating count). Missing this costs points. Ask yourself: what breaks a tie?
How long should the solution take to code?+
With the pattern clear, 10-15 minutes. One hash table loop, one sort call, one format pass. If you're still coding after 20 minutes, you misread the problem. Restart and reread the output spec.
Is this still a common Akuna interview question?+
Yes. Akuna repeats data aggregation problems across batches. The specific movie ratings variant changes slightly each cycle, but the hash table and sort skeleton stays the same. Expect it.