Choose Max Ratings
Reported by candidates from DE Shaw's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
DE Shaw's November OA included a problem called Choose Max Ratings. You're looking at a selection or optimization problem where the goal is to pick items (likely from an array or list) to maximize some scoring metric. The catch is usually constraint-based: limited picks, or dependencies between choices. If you blank on the exact greedy move or DP transition, StealthCoder reads the live problem and feeds you the pattern in seconds, so you're never stalled.
Pattern and pitfall
Without the full problem text, the pattern is likely greedy, dynamic programming, or a hybrid. Most "choose max" problems on OA platforms ask you to select K items to maximize a sum or score, subject to constraints like exclusions or ordering. The trap: naive greedy (just pick the biggest K) often fails because the constraint couples selections. DP usually wins here: state is (index, count picked, maybe last choice), transition is take-or-skip. If there's a dependency graph or conflict set, it's a weighted selection problem. On the live OA, if you're uncertain whether to DP or greedy, StealthCoder gives you the verdict instantly.
Drill it cold or hedge it with StealthCoder. Either way, don't walk into the OA hoping you remember the trick.
You can drill Choose Max Ratings cold, or you can hedge it. StealthCoder runs invisibly during screen share and surfaces a working solution in under 2 seconds. The proctor sees the IDE. They don't see what's behind it. Made for the candidate who got the OA invite this morning and has 72 hours, not six months.
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DE Shaw reuses patterns across OAs. Made for the candidate who got the OA invite this morning and has 72 hours, not six months. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Choose Max Ratings FAQ
Is this a greedy problem or DP?+
Greedy works if the best choice is always locally optimal regardless of what you picked before. If picking one item blocks or enables others, it's DP. Test the greedy heuristic on small examples during the OA. If it breaks, pivot to DP with a take-or-skip state.
What's the most common trap?+
Assuming you can sort once and pick the top K. Real constraints often couple the picks. Read the problem twice for exclusions, dependencies, or ordering rules before coding. One missed constraint costs the whole solution.
Can I solve this in 15 minutes?+
Yes, if it's greedy plus sorting. If it's DP, plan on 25-35 minutes for state design and testing. DE Shaw OAs usually give 45-60 minutes per problem, so you have room to iterate and debug.
What if I can't figure out the constraint during the OA?+
Re-read the problem statement word by word. Constraints are buried in phrases like 'adjacent items', 'disjoint sets', or 'one per category'. If truly stuck, code the greedy version first to show working logic, then refine.
Should I pre-study similar problems before the OA?+
Not worth it in 48 hours. Study classic greedy (interval scheduling, activity selection) and basic DP (knapsack) if you have time. The OA will be specific to DE Shaw's codebase anyway. Focus on reading the exact problem carefully live.