Exam AI-102 topic 1 question 50 discussion

This appeared in exam 28/06

if there is no metrics advisor, then choose Multivariate Anomaly detection as secondary option, Metrics Advisor is the best answer.

The correct choice is C. Multivariate Anomaly Detection. In this scenario, multiple engine sensor data points (such as rotation speed, angle, temperature, and pressure) are being monitored together, and they are likely interdependent. Multivariate Anomaly Detection can analyze these related variables simultaneously to detect anomalies that arise from their combined patterns. This makes it well-suited for identifying atypical values in complex systems like engines, where relationships between different variables are critical to detecting anomalies. A. Application Insights in Azure Monitor is mainly used for monitoring application performance and diagnostics. B. Metric alerts in Azure Monitor track specific metrics, but they are usually based on single variables rather than multivariate relationships. D. Univariate Anomaly Detection focuses on detecting anomalies in a single variable, which would not be as effective in this scenario where multiple interdependent variables are involved

"Multivariate Anomaly Detection" is needed.

Correct

C is right anwer.

C is right.

Please do not choose A, this is a machine learning topic and has nothing to do with Azure Monitor

C. Multivariate Anomaly Detection is the most comprehensive solution. It allows for the analysis of complex relationships between multiple metrics, which is crucial for accurately identifying anomalies in a system as intricate as an engine. This approach can help detect situations where the anomaly is not in the individual metrics but in their unexpected patterns or combinations.

https://learn.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/overview#multivariate-anomaly-detection

C is the answer. https://learn.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/overview#multivariate-anomaly-detection The Multivariate Anomaly Detection APIs further enable developers by easily integrating advanced AI for detecting anomalies from groups of metrics, without the need for machine learning knowledge or labeled data. Dependencies and inter-correlations between up to 300 different signals are now automatically counted as key factors. This new capability helps you to proactively protect your complex systems such as software applications, servers, factory machines, spacecraft, or even your business, from failures.

Multivariate Anomaly Detection - If your goal is to detect system level anomalies from a group of time series data, use multivariate anomaly detection APIs.

Anomaly detection does not support alerting though

The Multivariate Anomaly Detection APIs further enable developers by easily integrating advanced AI for detecting anomalies from groups of metrics, without the need for machine learning knowledge or labeled data. https://learn.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/overview#multivariate-anomaly-detection