Exam DP-100 topic 4 question 2 discussion

Answer is correct. AKS -> GPU support

AKS is the solution

B is correct! AKS supports real-time GPU-based inferencing https://learn.microsoft.com/en-gb/azure/machine-learning/concept-compute-target?view=azureml-api-2

B. Azure Kubernetes Service Azure Kubernetes Service (AKS) provides an option to create a GPU-enabled node pool which would be suitable for real-time GPU-based inferencing. The other services listed do not provide the same level of GPU support necessary for such operations. Keep in mind that setting up and managing a Kubernetes cluster does require some additional skills and setup compared to other Azure services, but it provides a high level of control and scalability.

The compute type that should be used for real-time GPU-based inferencing of a deep learning model is: D. Machine Learning Compute Explanation: To enable GPU-based inferencing on a deployed model, Machine Learning Compute (MLC) should be used. MLC is a managed service provided by Azure Machine Learning that can provision compute resources for training and inferencing machine learning models. The service can be configured to allocate resources based on the required processing power, which can include GPU and CPU-based clusters. Azure Container Instance (A) is a compute service that allows for running containerized applications without managing the underlying infrastructure, but it does not provide GPU resources. Azure Kubernetes Service (B) is a container orchestration service that can be used to manage containerized applications but requires additional configuration to enable GPU-based inferencing. Field Programmable Gate Array (C) is a hardware device that can be used to implement specific logic circuits but is not a cloud-based compute resource.

On 20Feb2022

Answer is correct.

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Answer is B. GPU for inference when deployed as a web service is supported only on AKS. https://docs.microsoft.com/en-gb/azure/machine-learning/concept-compute-target