Exam AI-102 topic 1 question 66 discussion

This is the similar example, I would vote for B. Use case of Stream Analytics Query: Alert to trigger a business workflow Let's make our query more detailed. For every type of sensor, we want to monitor average temperature per 30-second window and display results only if the average temperature is above 100 degrees. https://learn.microsoft.com/en-us/azure/stream-analytics/stream-analytics-get-started-with-azure-stream-analytics-to-process-data-from-iot-devices

The key requirements here are that you are doing this from a data stream and that you must limit development. Anomaly detection does not work with datastreams natively, so considerable development work would be required to integrate this functionality. As a result, the correct answer is C and not D.

B. Azure Stream Analytics While Univariate Anomaly Detection (D) is the concept of detecting anomalies in a single metric like temperature and is simpler than multivariate detection, the best way to minimize development effort in Azure is to use Azure Stream Analytics (B), which provides built-in univariate anomaly detection functions as part of its managed, scalable service. Stream Analytics allows you to process real-time data streams, detect anomalies with simple SQL-like queries, and integrate easily with alerting systems-all without building custom detection logic or managing infrastructure. Therefore, although univariate detection is the right approach conceptually, Azure Stream Analytics is the practical, low-effort solution to implement it end-to-end. Source: AI

To monitor temperature data from a data stream and generate alerts for atypical values with minimal development effort, you should include: B. Azure Stream Analytics Azure Stream Analytics allows you to process and analyze real-time data streams with minimal development effort. You can define query logic to detect anomalies and trigger alerts based on the results. This makes it an ideal solution for generating alerts in response to atypical temperature values from your data stream.

Because you’re dealing with a single time-series (temperature) and you want minimal development overhead for spotting anomalies, the best answer is: D. Univariate Anomaly Detection.

Confirmed by multiple AI-services

https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/overview#univariate-anomaly-detection Answer is D

Both ChatGPT and Copilot said.

Azure Stream Analytics offers the most straightforward and efficient solution for real-time temperature monitoring with minimal development effort

he correct answer is: D. Univariate Anomaly Detection Explanation: Since the system is monitoring temperature data from a data stream, and the goal is to generate alerts based on atypical values (likely unusual temperature readings), Univariate Anomaly Detection is the most suitable solution. Univariate Anomaly Detection focuses on identifying anomalies in a single time-series dataset. In this case, temperature data can be considered a univariate time-series, where the system detects unusual or atypical temperature values based on historical trends or thresholds. This minimizes development effort by using a simple approach to detect outliers in one variable (temperature) without requiring complex multi-variable or machine learning models.

At first read, I felt like "temperature data" refers to the broader category of any variables included to monitor temperature (such as humidity, elevation, etc). This is why I noted Multivariate Anomaly Detection. Azure Stream Analytics would require more configuration to build an anomaly detector so this doesn't sound like the best solution for "minimal development effort"

D. Explanation: The solution involves monitoring temperature data, which is typically a single-variable or univariate data stream. The Univariate Anomaly Detection service is ideal because: Focuses on Single Variables: It is optimized to detect anomalies in data streams consisting of a single variable, such as temperature readings. Minimal Development Effort: Azure's Anomaly Detector API includes Univariate Anomaly Detection and provides pre-trained models that require minimal customization or configuration. You only need to feed the data stream into the API and analyze the results. Efficient for Time Series Data: It detects sudden spikes, dips, or trends in time series data, which aligns perfectly with monitoring temperature anomalies.

if your primary goal is to detect anomalies and generate alerts with minimal development effort, Anomaly Detection might be the better choice. However, if you need to perform more complex real-time data processing and analytics, Stream Analytics could be more suitable.

Copoilot: For a solution that monitors temperature data from a data stream and generates alerts in response to atypical values while minimizing development effort, B. Azure Stream Analytics is the most suitable option. Here's why: Azure Stream Analytics provides a fully managed service for real-time data stream processing. It can easily integrate with other Azure services, making it straightforward to set up and scale. Built-in anomaly detection functions help identify outliers in data without the need for extensive custom development.

I asked Copilot : Overall, Azure Stream Analytics offers a more comprehensive and integrated solution for your requirements, making it easier to set up, maintain, and scale. And : Starting on the 20th of September, 2023 you won’t be able to create new Anomaly Detector resources

Best choice for the streams

Option A: Multivariate Anomaly Detection: Designed for detecting anomalies across multiple correlated variables. Since you're monitoring a single variable (temperature), it's unnecessarily complex and increases development effort. Option B: Azure Stream Analytics: Requires writing custom queries and potentially developing custom anomaly detection logic, adding to development effort. It doesn't offer built-in anomaly detection for atypical values out of the box. Option D: Univariate Anomaly Detection: Involves integrating the Anomaly Detector API, which requires additional coding, configuration, and maintenance compared to the straightforward setup of metric alerts in Azure Monitor.