scaling
AI, ML & GenAI interview questions tagged scaling, across every topic.
3 questions · 1 unlocked for you
Concepts behind "scaling"
The curriculum that explains the ideas these questions test.
Foundational
Foundation Models and the Pretrain-Adapt ParadigmA foundation model is a single large model pretrained on broad data and then adapted to many tasks, replacing the old habit of training one bespoke model per task. The scarce resource moved: it used to be labeled data and training compute, and now it is evaluation and context. AI, ML, and GenAI engineer interviews probe this because it explains why a prototype takes an afternoon while a reliable product still takes a quarter, and because knowing when a gradient-boosted tree still beats an LLM is a senior signal.🧠 Foundations of LLMs & GenAI
Foundational
Feature Engineering: Encoding, Scaling, SelectionFeature engineering is the work of turning raw columns into inputs a model can learn from: encoding categoricals, scaling numerics, and deciding which features to keep. Interviews probe it because it is the unglamorous lever that usually moves a metric more than swapping the model, and because the right choice hinges on cardinality, the model family, and leakage risk rather than on a default recipe.📊 Evaluation & ML Foundations
Core
Mixture-of-ExpertsA Mixture-of-Experts model swaps the dense feed-forward layer for many expert networks plus a router that sends each token to only a few of them. This separates total parameters (capacity) from per-token compute: the model can be huge while each token activates only a slice. The trade-offs are routing complexity, memory (all experts must be loaded), and load balancing. AI, ML, and GenAI interviews probe it because most frontier models are MoE, and it explains how models grow more capable without proportionally more inference cost.🧠 Foundations of LLMs & GenAISign in