02When do you choose prompting vs RAG vs fine-tuning for a customer problem?▼medium★ EssentialOpenAIAnthropicCohere2 repliesunlockedThe most frequently asked applied GenAI question, and the one most candidates turn into a definition dump. What the interviewer wants is a decision framework with a default and the conditions that override it. Here is the call that earns points.Open full answer →
02Explain the bias-variance tradeoff, and how you diagnose and fix high bias vs high variance.▼medium★ EssentialAmazonGoogleMeta2 repliesunlockedThe most frequent ML fundamentals question, and a subtle seniority check: anyone can repeat the definition, but can you break down the error and convert it into a concrete debugging plan?Open full answer →
78What assumptions does linear regression make, and how do you check and handle violations?▼mediumAmazonMetaDatabricks1 replies◆ premiumAnyone can recite 'linearity and normality.' Few can say which assumption matters for predictions versus inference, how to catch a violation in a residual plot, and what to actually do about it. Here is that answer.Open full answer →
57What is AI Engineering, and how is it different from Machine Learning Engineering?▼easyOpenAIAnthropicDatabricks◆ premiumThe opener in half of all Applied AI screens, and most candidates answer it with a definition nobody scores. The distinction that actually earns points is about where you start and what your bottleneck turns out to be.Open full answer →
