40How do you detect outliers and anomalies, and which method do you choose?▼mediumAmazonGoogleMicrosoft1 replies◆ premiumOutlier detection appears in data cleaning, fraud, and monitoring, and no single method wins everywhere. Interviewers want you to match the statistical, distance, or model-based families to the dimensionality, distribution, and labels you actually have.Open full answer →
118How do you build anomaly detection for a streaming time series, and how do you handle seasonality and concept drift?▼hardNetflixUberMicrosoft2 replies◆ premiumThreshold alarms page you all weekend yet sleep through Monday's actual outage. What interviewers reward is stripping out seasonality first, matching the detector to the anomaly, and tuning against alert fatigue. Here is the answer that holds up in production.Open full answer →
10Design an anomaly detection system for a metric (e.g. cloud billing) with seasonality and cold start.▼hardAmazonMicrosoftGoogle1 repliesunlockedAnomaly detection seems simple until seasonality, cold start, and alert fatigue arrive. The signal is modeling the expected baseline (including weekly and daily cycles), picking unsupervised methods when labels are scarce, and tuning so you do not bury users in false alarms. Here is the design.Open full answer →
