Chair Requirements
Reported by candidates from Goldman Sachs's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
Goldman Sachs asked this in January 2024, and it's a constraint-satisfaction problem that looks deceptively simple on the surface. You're given requirements for chairs (likely dimensions, materials, or specifications), and you need to determine if they can all be met simultaneously or assign them optimally. The trick isn't the math. It's recognizing whether you're solving a feasibility check, a resource allocation problem, or a matching problem. StealthCoder is your safety net if you misread the constraint structure in the first 30 seconds.
Pattern and pitfall
Chair Requirements is almost certainly a constraint-satisfaction or greedy assignment problem. The pattern hinges on reading carefully: are you checking if all requirements can coexist, or are you optimizing an allocation given conflicting demands. Common pitfall is treating it as a pure math problem when it's actually a logic or greedy problem. You'll likely iterate through requirements, track conflicts or capacities, and either return a boolean or an optimal assignment. The Goldman Sachs twist is often in the efficiency of your solution under large input. If you blank on the approach, a brute-force feasibility check buys time, but recognize early whether you can prune or greedy-pick.
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Chair Requirements FAQ
Is this a constraint-satisfaction or optimization problem?+
The title and company suggest both. Read the first example carefully. If it asks 'can all be satisfied', it's feasibility. If it asks 'assign optimally', it's optimization. The problem statement will be explicit about what you're returning.
What's the common mistake candidates make?+
Assuming all requirements are independent. They're not. One requirement often blocks another, or resources are limited. Map out the conflicts before coding. A two-minute dependency diagram saves 15 minutes of backtracking.
How do I know if greedy works here?+
Try greedy on the examples first. Sort by a property (strictest requirement first, most common last, etc.) and assign greedily. If it passes examples, it's likely correct. If not, you need dynamic programming or backtracking.
What if I time out?+
You're probably brute-forcing all permutations. Switch to greedy or constraint propagation. Goldman Sachs expects O(n log n) or O(n^2) at worst, not exponential. Prune aggressively.
Should I prepare a specific algorithm for this?+
No. Understand constraint satisfaction, greedy selection, and basic graph traversal for conflict detection. The specific algorithm will emerge from the problem statement. Read it twice before coding.