Maximize Items
A hard-tier problem at 75% community acceptance, tagged with Database. Reported in interviews at HashedIn and 0 others.
Maximize Items is a database problem that's been asked at HashedIn. The 75% acceptance rate is deceptive, that's self-selection bias. The candidates who submit solutions are already confident, which means the jump from easy database queries to this problem is real. If you're hitting HashedIn's online assessment and haven't drilled SQL optimization or aggregation tricks, you'll recognize the pattern only after you've written three wrong queries. StealthCoder surfaces the right approach in seconds, invisible to the proctor, so you don't waste twenty minutes backtracking.
Companies that ask "Maximize Items"
Maximize Items 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 a senior engineer who knows the OA is theater. This is the script.
Get StealthCoderMaximize Items is a classic optimization query, you need to find the maximum or optimal set of items under some constraint, typically using GROUP BY, window functions, or subquery logic to rank and filter. The trap is thinking about the problem in the order you read it. Most candidates write a straightforward join or WHERE clause, then realize their result set is either too large, doesn't isolate the true maximum per group, or counts duplicates. The fix usually involves either ranking within partitions (ROW_NUMBER or DENSE_RANK) or writing a subquery that finds the max in one layer and filters in another. Database problems punish half-solutions hard. StealthCoder is the hedge if you blank on whether to use a CTE, window function, or nested SELECT, it executes and shows you which one the test suite actually wants.
Pattern tags
You know the problem.
Make sure you actually pass it.
Maximize Items 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 a senior engineer who knows the OA is theater. This is the script. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximize Items interview FAQ
How hard is Maximize Items really if I'm solid at basic joins?+
Basic joins aren't enough. You need to think about aggregation scope and ranking. The problem forces you to decide: do I filter rows first, or do I rank then filter. Pick wrong and your result doubles. If you've built production queries, you'll recognize the pattern. If you haven't, it's a stumble.
Is this still asked at real companies beyond HashedIn?+
HashedIn is the only report we have in the data. But optimization and aggregation queries like this are table stakes for any database role. If your OA includes SQL, assume you'll see this flavor of problem.
What's the main trick to Maximize Items?+
Isolate the maximization logic. Most candidates write one query. The safe approach: subquery to find the max value per group, then join or filter to that. Separates concerns, avoids accidental double-counting. Window functions are faster to read, but subqueries are faster to debug under pressure.
Will I have time to test different query approaches during the assessment?+
Probably not. You get one shot at the right logic, and the test suite runs instantly. A wrong query fails multiple test cases at once. That's why knowing the pattern before you sit down matters, and why StealthCoder is a safety net if the pattern doesn't click.
Should I practice with GROUP BY and window functions before my OA?+
Yes. Both. GROUP BY with HAVING filters by aggregate. Window functions rank or cumulate without reducing rows. Maximize Items likely needs both concepts or a choice between them. If you can write both and know when each one breaks, you're ready.
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