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Exam DP-100 topic 3 question 70 discussion

Actual exam question from Microsoft's DP-100

Question #: 70
Topic #: 3

DRAG DROP -
You create machine learning models by using Azure Machine Learning.
You plan to train and score models by using a variety of compute contexts. You also plan to create a new compute resource in Azure Machine Learning studio.
You need to select the appropriate compute types.
Which compute types should you select? To answer, drag the appropriate compute types to the correct requirements. Each compute type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:

Show Suggested Answer

Suggested Answer:

Box 1: Compute cluster -
Create a single or multi node compute cluster for your training, batch inferencing or reinforcement learning workloads.

Box 2: Inference cluster -

Box 3: Attached compute -
The compute types that can currently be attached for training include:

A remote VM -
Azure Databricks (for use in machine learning pipelines)
Azure Data Lake Analytics (for use in machine learning pipelines)

Azure HDInsight -

Box 4: Compute cluster -
Note: There are four compute types:

Compute instance -

Compute clusters -

Inference clusters -

Attached compute -
Note 2:

Compute clusters -
Create a single or multi node compute cluster for your training, batch inferencing or reinforcement learning workloads.

Attached compute -
To use compute targets created outside the Azure Machine Learning workspace, you must attach them. Attaching a compute target makes it available to your workspace. Use Attached compute to attach a compute target for training. Use Inference clusters to attach an AKS cluster for inferencing.

Inference clusters -
Create or attach an Azure Kubernetes Service (AKS) cluster for large scale inferencing.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio

by ac45863 at April 7, 2021, 11:05 p.m.

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syed_ahmed

Highly Voted 3 years, 6 months ago

This is the correct answer : Compute Inference Attached Inference

upvoted 52 times

gunn_m

7 months, 2 weeks ago

We deploy the model to an inference cluster, but to perform the deployment action we need a cluster or an instance.

upvoted 1 times

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DennisWitjes

3 years, 5 months ago

Agreed. When deploying in AML Designer you have to select inference cluster. See example where they use AKS. https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy

upvoted 2 times

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htiwari

Highly Voted 3 years, 9 months ago

This answer is correct! https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target

upvoted 7 times

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deyoz

Most Recent 12 months ago

For the deployment is it inference or compute cluster, I understand as inference but folks are saying compute cluster.

upvoted 1 times

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A_PL300

1 year, 4 months ago

Question like this one on September 4, 2023 exam

upvoted 2 times

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Yuriy_Ch

1 year, 10 months ago

on exam 07/March/2023

upvoted 1 times

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phdykd

1 year, 11 months ago

Requirement A: To train models by using the Azure Machine Learning designer, you can use a compute cluster. This will allow you to allocate resources to run your training jobs in a distributed fashion, enabling you to complete training faster and at a lower cost. Requirement B: To score new data through a trained model published as a real-time web service, you can use an inference cluster. This will allow you to deploy your model as a scalable web service that can handle incoming requests for real-time predictions. Requirement C: To train models by using an Azure Databricks cluster, you can use both an attached compute and a compute cluster. An attached compute allows you to connect your Azure Machine Learning workspace to your Databricks workspace, while a compute cluster allows you to allocate resources to run your training jobs in a distributed fashion. Requirement D: To deploy models by using the Azure Machine Learning Designer, you can use a compute cluster. This will allow you to allocate resources to run your deployment job, enabling you to complete the deployment faster and at a lower cost.

upvoted 1 times

deyoz

11 months, 3 weeks ago

but realtime deployment doesn't work in compute cluster. Hence, inference cluster. In my opinion. i am still not sure tough.

upvoted 1 times

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phdykd

1 year, 11 months ago

Requirement A: 3- Compute cluster Requirement B: 2- Inference cluster Requirement C: 1-Attached compute 3- Compute cluster Requirement D: 3- Compute cluster

upvoted 2 times

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racnaoamo

2 years, 8 months ago

on exam 18-5-22

upvoted 3 times

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kkkk_jjjj

2 years, 10 months ago

on exam 18/03/2022

upvoted 2 times

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JoshuaXu

3 years, 2 months ago

on Exam 6 Nov 2021

upvoted 2 times

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pkal

3 years, 4 months ago

on exam 9/24/2021

upvoted 1 times

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azure1000

3 years, 5 months ago

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-model-designer last one is Inference cluster **In the Models asset page, select the registered model. Select the Deploy button. In the configuration menu, enter the following information: Input a name for the endpoint. Select to deploy the model to Azure Kubernetes Service or Azure Container Instance. **

upvoted 6 times

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