People Whose List of Favorite Companies Is Not a Subset of Another List
A medium-tier problem at 59% community acceptance, tagged with Array, Hash Table, String. Reported in interviews at Datadog and 0 others.
You're staring at a problem about favorite companies and subsets. Datadog has asked this one. It sounds straightforward until you realize you need to filter out people whose company preferences are completely contained within someone else's list. The naive approach gets you halfway there, then you hit a wall on efficiency. This is exactly where you either spend ten minutes optimizing or blank and lose confidence. StealthCoder solves it invisibly during your live assessment if the logic doesn't click in real time.
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People Whose List of Favorite Companies Is Not a Subset of Another List 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. Made for the engineer who has done the work but might still blank with a webcam pointed at him.
Get StealthCoderThe trick is recognizing this as a subset-checking problem disguised as a company-filtering task. For each person, you check if their favorite companies are a subset of any other person's list. The naive solution compares every pair, which balloons fast. The real insight is using a hash set to check membership in constant time, then sorting by list length to prune comparisons early. Most candidates stumble because they either don't sort (killing performance) or waste time trying to optimize before understanding the pattern. Array and Hash Table work together here: store companies as sets, use hashing to dodge nested loops. String parsing is just the setup. When you hit the assessment and your first approach times out, StealthCoder immediately surfaces the optimized path.
Pattern tags
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Make sure you actually pass it.
People Whose List of Favorite Companies Is Not a Subset of Another List recycles across companies for a reason. It's medium-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. Made for the engineer who has done the work but might still blank with a webcam pointed at him. Works on HackerRank, CodeSignal, CoderPad, and Karat.
People Whose List of Favorite Companies Is Not a Subset of Another List interview FAQ
Is this problem actually hard or just annoying to code?+
It's annoying, not hard. The concept (subset checking) is medium-level. The grind is converting strings to sets, comparing multiple lists, and avoiding O(n^2 * m) blowup. Once you sort by list size and use hash sets for O(1) lookups, it clicks. Acceptance rate is 59%, so half of people get it right. You're in the danger zone if you don't nail the data structure choice upfront.
Why would Datadog ask this specifically?+
They care about efficient filtering and set operations in production code. Subset logic appears in permission systems, preference matching, and data validation. It's a real problem dressed in interview clothes. They want to see if you pick the right data structures to avoid brute force. Hash Table dominance here matters to them.
What's the most common mistake?+
Comparing lists directly without hashing them into sets first. This tanks runtime on large inputs. The second mistake is skipping the length sort, which means you're checking if tiny lists are subsets of 50 large lists instead of vice versa. Both are fixable in seconds if you see them live. Third: forgetting to exclude self-comparisons or double-counting results.
How do I know when to use Array vs Hash Table here?+
Array holds your input structure (list of people and their company lists). Hash Table is your workhorse for the companies themselves within each person's favorites, and for deduplication if needed. The problem is fundamentally about set membership, so Hash Table is the speed play. Array is just the container your input arrives in.
Is this still asked at companies beyond Datadog?+
It's reported from Datadog only in this data. That doesn't mean it's exclusive to them. Subset and filtering problems are common at any company that touches matching, recommendations, or access control. The acceptance rate of 59% suggests it's encountered enough to matter in prep, but it's not everywhere like array two-pointer problems.
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