simulation
AI, ML & GenAI interview questions tagged simulation, across every topic.
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Concepts behind "simulation"
The curriculum that explains the ideas these questions test.
Core
Synthetic Data GenerationSynthetic data is training or eval data made by a model, a simulator, or a program instead of gathered from the real world, used to bootstrap labels, cover rare cases, and distill a larger model down into a smaller one. Whether it helps depends on quality, diversity, and keeping leakage out between your generator and your eval. AI, ML, and GenAI engineer interviews probe it because candidates grab it as a free fix and overlook the failure modes: distribution mismatch, eval contamination, and model collapse from training on a model's own outputs.📊 Evaluation & ML FoundationsSign in
Core
Matrix and Grid Simulation PatternsGrid problems reward a small set of mechanical patterns: walk a spiral by shrinking four boundaries, rotate a square in place with a transpose-then-reverse, and store state inside the grid itself to keep extra space at O(1). The hard part is index bookkeeping, not algorithms. Interviews test these because off-by-one errors on boundaries are where most candidates lose points, and in-place tricks check whether you can dodge an obvious extra-memory copy.💻 Coding & Engineering CraftSign in