Identify the Largest Outlier in an Array
A medium-tier problem at 36% community acceptance, tagged with Array, Hash Table, Counting. Reported in interviews at Goldman Sachs and 0 others.
Goldman Sachs has asked this problem consistently, and it's a pattern-detection question that catches candidates off-guard. You're given an array and need to find the largest value that doesn't fit the expected numeric pattern. It sounds straightforward until you realize the "expected pattern" isn't spelled out in the problem statement. You have to infer it from the data itself. The acceptance rate sits at 36%, which means most people either miss the trick on first read or implement a solution that doesn't account for edge cases. If this problem hits your live assessment and you blank on what "outlier" actually means in context, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
Companies that ask "Identify the Largest Outlier in an Array"
Identify the Largest Outlier in an Array 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 by a working Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE.
Get StealthCoderThe core trick is recognizing that an outlier in this context usually means a value that breaks an implicit arithmetic or XOR property of the array. Most candidates jump to statistical approaches (standard deviation, median distance) and waste time. The real solution is often much simpler: use a Hash Table to count frequencies and identify the value that appears a different number of times than others, or use XOR/bit-level tricks if the array has a numeric invariant. The problem tests whether you can reverse-engineer intent from messy data rather than apply a pre-memorized algorithm. Common pitfall: implementing frequency counting correctly but then selecting the wrong outlier when multiple candidates exist. Another mistake: assuming the outlier is always the maximum or minimum. StealthCoder becomes your safety net if you freeze on interpretation during the live OA.
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Identify the Largest Outlier in an Array 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 by a working Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Identify the Largest Outlier in an Array interview FAQ
Why is the acceptance rate so low at 36 percent?+
The problem statement is intentionally vague about what defines an outlier. Candidates misinterpret the pattern, implement the wrong logic, or handle edge cases poorly. Once you grasp the actual pattern (usually frequency-based or XOR-based), it becomes straightforward. The low rate reflects the interpretation trap, not inherent difficulty.
What's the difference between this and a standard frequency-counting problem?+
Standard frequency problems ask you to count or rank known items. This problem requires you to infer the rule first. You're using Hash Table and Counting skills, but the setup demands pattern recognition and enumeration of possibilities before you can apply those techniques.
Is this still asked at Goldman Sachs?+
Goldman Sachs appears as the reported source. Tech interviews at large financial firms rotate problems, but pattern-detection and hash-table logic are evergreen. If you're interviewing there, this problem is a realistic scenario.
How do I know if my approach is right before submitting?+
Test your logic on a small example where you can manually verify the outlier. If your solution identifies the correct outlier and also explains why it's an outlier (not just finds a max or min), you're on track. Walk through your Hash Table or enumeration step by step.
What topics should I brush up on before tackling this?+
Review Hash Table operations (counting, lookups) and Enumeration patterns. Understanding XOR properties and how frequency distributions work helps too. The Array manipulation itself is light; the heavy lifting is in the pattern inference and counting logic.
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