Consider the following staff-level ML design interview questions. Are there any major areas that are not covered here, and that would be fair game for a generalist MLE who will be directly involved in modeling?
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You’re tasked with designing a fraud detection system for an e-commerce platform. The system needs to adapt to evolving fraud patterns without frequent manual intervention. How would you approach the design of this system to ensure it remains effective over time? Consider data sources, model architecture, and strategies for handling concept drift.
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Design a real-time ad targeting system for a mobile gaming platform where users are shown interstitial ads between game levels. The ads could be for in-game purchases, other games, or external products and services. The system should maximize user engagement and ad revenue while respecting user privacy and maintaining low latency. How would you design the model to handle real-time decisions, and how would you incorporate user behavior data while adhering to privacy regulations like GDPR?
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You are tasked with building a multi-modal search engine for an online marketplace that allows users to search for products using text descriptions, images, and voice queries. The marketplace includes a wide range of products, from electronics to fashion items. How would you design a system that accurately retrieves relevant products across these different query types? What specific challenges do you anticipate in aligning these different modalities, and how would you address them?
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Design a real-time bidding (RTB) system for serving display ads on a network of news websites. The ads could range from generic banners to personalized offers based on user profiles. The challenge is to balance the need for fast decision-making with the complexity of predicting which ads will generate the most clicks or conversions. How would you structure the model to ensure low latency while still incorporating user behavior and contextual data (e.g., the type of news article) into your predictions?
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You’re tasked with building a content personalization engine for a news aggregator app that serves users with a personalized feed of articles. The engine must not only consider the user’s reading history but also incorporate real-time data on trending topics and breaking news. How would you design this system to ensure that the content remains relevant to the user while also being timely and engaging? Discuss how you would handle model updates, data integration from various news sources, and real-time inference.
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You are tasked with designing a predictive maintenance system for a fleet of industrial equipment, where each machine generates time-series sensor data. However, the data is very sparse because the machines are rarely in failure mode, and failures happen infrequently. How would you design the model to predict equipment failures with limited labeled data? What strategies would you use to handle the sparsity and imbalance of the failure events, and how would you ensure the system remains scalable as the number of machines grows? Additionally, how would you implement model monitoring to track performance over time, especially given that the data distribution might change as the machines age?