Stop grinding random LeetCode. For Google MLE, 81 questions cover most of what actually gets asked. Here's the breakdown from the 2026 reported questions, by where they actually land: 1. Coding (the biggest bucket): graphs, DP, and arrays. Graph traversal and dynamic programming show up again and again — not as textbook exercises, but wrapped in product-flavored stories (recommendation feeds, search ranking, delivery routing). If you can do BFS/DFS blindfolded and spot the DP state in under a minute, you're ahead of most candidates. 2. ML system design: ranking and retrieval. Expect to design a ranking pipeline end to end: candidate generation, feature engineering, model serving, and offline/online metric gaps. Interviewers care less about naming the fanciest model and more about trade-offs: latency vs. recall, freshness vs. cost. 3. ML fundamentals: the questions that filter people out. Transformer attention math, why LayerNorm placement matters, what actually breaks when you scale batch size, and how to read a precision/recall trade-off in an imbalanced setting. Short answers, no hand-waving — this is where preparation shows. How I'd use this list: go category by category, not question by question. Master one pattern family per week, then do a timed mock on that family before moving on. Full checklist with all 81 questions, frequency notes, and category breakdown: https://mleplaybook.gumroad.com/l/bznrnw ($19)