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DP-100 – Designing and Implementing a Data Science Solution on Azure

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Showing 7–9 of 20 questions

Question 7 (New Update)

You are developing a machine learning model.

You must inference the machine learning model for testing.

You need to use a minimal cost compute target

Which two compute targets should you use? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point

Select all that apply, then click Submit answer.

  • ○

    Local web service
  • ○

    Remote VM
  • ○

    Azure Databricks
  • ○

    Azure Machine Learning Kubernetes
  • ○

    Azure Container Instances
Question 8 (New Update)

You plan to provision an Azure Machine Learning Basic edition workspace for a data science project.

You need to identify the tasks you will be able to perform in the workspace.

Which three tasks will you be able to perform? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

D

Select all that apply, then click Submit answer.

  • ○

    Create a Compute Instance and use it to run code in Jupyter notebooks.
  • ○

    Create an Azure Kubernetes Service (AKS) inference cluster.
  • ○

    Use the designer to train a model by dragging and dropping pre-defined modules.
  • ○

    Create a tabular dataset that supports versioning.
  • ○

    Use the Automated Machine Learning user interface to train a model.
Question 9 (Mixed Questions)

You use the Azure Machine Learning Python SDK to define a pipeline that consists of multiple steps.

When you run the pipeline, you observe that some steps do not run. The cached output from a previous run is used instead.

You need to ensure that every step in the pipeline is run, even if the parameters and contents of the source directory have not changed since the previous run.

What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

Select all that apply, then click Submit answer.

  • ○

    Use a PipelineData object that references a datastore other than the default datastore.

  • ○

    Set the regenerate_outputs property of the pipeline to True.

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    Set the allow_reuse property of each step in the pipeline to False.

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    Restart the compute cluster where the pipeline experiment is configured to run.

  • ○

    Set the outputs property of each step in the pipeline to True.