nerdexam
Microsoft

DP-100 · Question #358

You are creating a compute target to train a machine learning experiment. The compute target must support automated machine learning, machine learning pipelines, and Azure Machine Learning designer…

The correct answer is B. Azure Machine Learning compute cluster. To create a compute target in Azure Machine Learning that supports automated ML, ML pipelines, and designer training, an Azure Machine Learning compute cluster is the optimal choice.

Design and prepare a machine learning solution

Question

You are creating a compute target to train a machine learning experiment. The compute target must support automated machine learning, machine learning pipelines, and Azure Machine Learning designer training. You need to configure the compute target. Which option should you use?

Options

  • AAzure HDInsight
  • BAzure Machine Learning compute cluster
  • CAzure Batch
  • DRemote VM

How the community answered

(25 responses)
  • A
    4% (1)
  • B
    92% (23)
  • D
    4% (1)

Why each option

To create a compute target in Azure Machine Learning that supports automated ML, ML pipelines, and designer training, an Azure Machine Learning compute cluster is the optimal choice.

AAzure HDInsight

Azure HDInsight is primarily a big data analytics service and while data processing for ML can occur there, it's not the native or most integrated compute target for training within Azure ML's specific features like autoML or designer.

BAzure Machine Learning compute clusterCorrect

An Azure Machine Learning compute cluster (AmlCompute) is a managed compute infrastructure fully integrated with Azure Machine Learning services. It is specifically designed to support scalable training for automated machine learning, machine learning pipelines, and experiments executed through the Azure Machine Learning designer.

CAzure Batch

Azure Batch is a job scheduling service for large-scale parallel and high-performance computing, which can be used for ML but requires more manual integration and lacks the native support for autoML, pipelines, and designer that AmlCompute offers.

DRemote VM

A Remote VM can be attached as a compute target, but it lacks the managed scalability, auto-scaling capabilities, and deep integration with autoML and designer provided by an Azure Machine Learning compute cluster.

Concept tested: Azure ML compute targets for training

Source: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-cluster?view=azureml-api-2

Topics

#Azure Machine Learning#Compute Target#Automated ML#ML Pipelines

Community Discussion

No community discussion yet for this question.

Sign up to join the discussion
Full DP-100 Practice