Closest Landmark
Reported by candidates from Meta's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
Meta's Closest Landmark question hit assessments in February 2024, and it's testing your ability to handle spatial logic under pressure. You're likely dealing with coordinate geometry or distance calculations, which sounds simple until you're staring at the problem cold and your brain stalls on edge cases. StealthCoder sits invisible during your OA and feeds you the pattern in real time, so you're never stuck guessing whether to use Euclidean distance, Manhattan distance, or some custom metric the problem defines.
Pattern and pitfall
This problem revolves around finding the nearest point or landmark in a dataset relative to a query position. The pattern typically involves either brute-force distance calculation with a sorted result, or spatial indexing if the dataset is large. Common pitfalls: candidates forget to handle ties (multiple landmarks at the same distance), misread the distance formula, or fail to consider negative coordinates. The trick is almost always in the distance metric itself. Read the problem statement three times to confirm whether it's Euclidean (square root of sum of squared differences), Manhattan (sum of absolute differences), or something custom. StealthCoder will parse the exact definition from the screen and surface the right formula, so you avoid the derailment that costs most candidates here.
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Closest Landmark FAQ
What distance formula does Meta usually ask for?+
It depends on the exact problem text, but Euclidean distance is most common for 'landmark' problems. Always confirm in the problem statement itself. If it says 'closest' without specifying, assume Euclidean. Manhattan distance is less common but still appears.
How do I handle ties if two landmarks are equidistant?+
The problem will specify tiebreaker logic, often by lexicographic order, by landmark ID, or by a secondary attribute. Don't assume. Skim the problem for 'in case of a tie' or 'if distance is equal'. That clause is always there.
Is this a brute-force or advanced data structure question?+
Brute-force (iterate all landmarks, compute distance, return min) will pass most constraint sizes. Only optimize to KD-tree or similar if the problem explicitly states 'multiple queries' and large datasets. Meta usually accepts the simpler approach.
Should I precompute distances or calculate on demand?+
For a single query, calculate on demand. For multiple queries on a static landmark set, precompute and sort once, then answer queries in O(1). The problem will hint at this. Read the input format carefully.
Can I skip negative coordinates or edge case testing?+
No. Landmarks can be in any quadrant. Test with negative x, negative y, origin (0,0), and identical coordinates. Off-by-one errors and sign mistakes are the killers here.