Exam AI-100 topic 4 question 1 discussion

Actual exam question from Microsoft's AI-100

Question #: 1
Topic #: 4

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
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You are deploying an Azure Machine Learning model to an Azure Kubernetes Service (AKS) container.
You need to monitor the scoring accuracy of each run of the model.
Solution: You configure Azure Monitor for containers.
Does this meet the goal?

A. Yes
B. No

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Suggested Answer: B 🗳️

by dijaa at Aug. 23, 2021, 3:13 p.m.

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rveney

2 years ago

A. Yes Configuring Azure Monitor for containers would meet the goal of monitoring the scoring accuracy of each run of the model deployed to an Azure Kubernetes Service (AKS) container. Azure Monitor for containers provides monitoring capabilities specifically designed for containerized workloads running on AKS. It allows you to collect and analyze performance metrics, logs, and events from your AKS cluster, including the containers running your Azure Machine Learning model. By configuring Azure Monitor for containers, you can gain visibility into the performance and behavior of your model. You can monitor key metrics such as CPU and memory usage, network traffic, and container health. Additionally, you can set up alerts based on specific conditions or thresholds, enabling you to proactively monitor the scoring accuracy and detect any anomalies or issues that may arise.

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rveney

2 years ago

B. No The solution mentioned does not meet the goal of monitoring the scoring accuracy of each run of the Azure Machine Learning model deployed to an Azure Kubernetes Service (AKS) container. Azure Monitor for containers is designed to monitor the performance, health, and utilization of containers running in Azure Kubernetes Service. While it provides valuable insights into container-level metrics and logs, it does not specifically focus on monitoring the scoring accuracy of a machine learning model. To monitor the scoring accuracy of the model, you would need to implement additional monitoring and logging within your application code. This could involve logging the predictions and comparing them to the ground truth values, setting up custom metrics, or leveraging Azure Application Insights for more detailed monitoring and analysis.

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dijaa

3 years, 10 months ago

solution: app insights

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claudiapatricia777

3 years, 7 months ago

The response is No correct, but the solution would be using a DataDrift Monitor https://www.analyticsvidhya.com/blog/2021/10/mlops-and-the-importance-of-data-drift-detection/

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