Onsite Interview Questions
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eBay Browser Rendering Interview: Rendering Pipeline Deep Dive
Learn the browser rendering pipeline (DOM/CSSOM → render tree → layout → paint), spot performance bottlenecks, and use DevTools to prep for eBay interviews.
eBay Coding Question: Increasing Triplet Subsequence
Check if an array contains a strictly increasing subsequence of length 3. Study the O(n) greedy/two-pointer solution, edge cases, and interview tips. Try it.
eBay CS Fundamentals: Cache Memory Write Policies Explained
Understand write-back vs write-through cache policies, write allocation and coherence for eBay software engineer interviews. Read examples and prep answers now.
eBay ML System Design: Post-Checkout Recommendations
Design a low-latency post-checkout recommendation system for eBay: retrieval, ranking, features, training, and metrics. Prepare for ML interviews with examples.
eBay System Design: Concert Ticketing with Real-Time
Design a scalable concert ticketing backend for eBay: prevent oversells, ensure <100ms reads, integrate social views, and handle peak traffic. Learn the design.
From-Scratch PyTorch Transformer — Apple Interview
Implement a runnable, from-scratch PyTorch Transformer (encoder–decoder) with Multi-Head Attention, masks, and residuals. Read steps, tips, and follow-ups.
Google Behavioral Interview: Problem Solving & Ambiguity
Master Google's behavioral problem-solving question: clarify ambiguity, weigh trade-offs, and show corrective actions with examples. Prepare examples.
Google Coding Question: Online Longest Subarray with Average
Track longest contiguous subarray with average S online. Use prefix-sum key = pref - S*i with a hashmap. Read precision tips and complexity notes.
Google Coding Question: Task Scheduling with Precedence
Minimal makespan for DAG tasks with durations and precedence constraints on unlimited or M CPUs. Includes algorithms, complexity notes, and follow-ups.
Google CS: Memory Management & Go Slices Interview
Prep for Google's memory management interview: study virtual memory, paging, page faults, and Go slices. Get tips, examples, and follow-ups to practice.
Google ML Coding: Hand-code Multi-Head Attention in NumPy
Implement multi-head attention in NumPy: scaled dot-product for batched Q,K,V. Do per-head projections, reshape, apply mask, and return attention weights.
Google ML Foundations Interview: Loss Functions Guide
Prepare for Google ML interviews: learn MSE vs cross-entropy, derive gradients, and handle numerical stability and class imbalance. Practice follow-ups and choose the right loss.
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