Q: 15
You plan to perform predictive maintenance. You collect IoT sensor data from 100 industrial machines for a year. Each machine has 50 different sensors that generate data at one-minute intervals. In total, you have 5,000 time series datasets. You need to identify unusual values in each time series to help predict machinery failures. Which Azure Cognitive Services service should you use?
Options
Discussion
A imo. I've seen similar questions in other sets and Anomaly Detector is the main Azure service for time series outlier detection, especially when you want to surface unusual sensor readings for predictive maintenance. B (Cognitive Search) is great for searching content but doesn't do anomaly ML itself. Anyone disagree?
B tbh, Cognitive Search seems logical for searching sensor logs if the focus is quick lookups, not just automatic outlier detection. Trap for those thinking only pure anomaly detection matters here.
Its A, Anomaly Detector fits the time series anomaly use case. B is more for indexing/search, doesn't detect outliers automatically.
Option A
I could see B, Cognitive Search, being picked if you’re thinking about querying and filtering sensor logs for unusual events. Makes sense in log-heavy scenarios.
For predictive maintenance with time series data, wouldn't Cognitive Search (B) help here by indexing and searching sensor logs? Had something like this in a mock where finding patterns and outliers was needed, but I assumed search could spot anomalies through query filters. Pretty sure B is reasonable if we need fast lookups. Am I off here?
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