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Question 9 - DP-100 discussion

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An organization creates and deploys a multi-class image classification deep learning model that uses a set of labeled photographs.

The software engineering team reports there is a heavy inferencing load for the prediction web services during the summer. The production web service for the model fails to meet demand despite having a fully-utilized compute cluster where the web service is deployed.

You need to improve performance of the image classification web service with minimal downtime and minimal administrative effort.

What should you advise the IT Operations team to do?

A.
Create a new compute cluster by using larger VM sizes for the nodes, redeploy the web service to that cluster, and update the DNS registration for the service endpoint to point to the new cluster.
Answers
A.
Create a new compute cluster by using larger VM sizes for the nodes, redeploy the web service to that cluster, and update the DNS registration for the service endpoint to point to the new cluster.
B.
Increase the node count of the compute cluster where the web service is deployed.
Answers
B.
Increase the node count of the compute cluster where the web service is deployed.
C.
Increase the minimum node count of the compute cluster where the web service is deployed.
Answers
C.
Increase the minimum node count of the compute cluster where the web service is deployed.
D.
Increase the VM size of nodes in the compute cluster where the web service is deployed.
Answers
D.
Increase the VM size of nodes in the compute cluster where the web service is deployed.
Suggested answer: B

Explanation:

The Azure Machine Learning SDK does not provide support scaling an AKS cluster. To scale the nodes in the cluster, use the UI for your AKS cluster in the Azure Machine Learning studio. You can only change the node count, not the VM size of the cluster.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-kubernetes

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asked 02/10/2024
Amin Dashti
51 questions
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