DP-100 Questions Bank

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You are a data scientist using Azure Machine Learning Studio.

You need to normalize values to produce an output column into bins to predict a target column.

Solution: Apply a Quantiles binning mode with a PQuantile normalization.

Does the solution meet the goal?

You have an Azure Machine Learning workspace named workspace1 that is accessible from a public endpoint. The workspace contains an Azure Blob storage datastore named store1 that represents a blob container in an Azure storage account named account1. You configure workspace1 and account1 to be accessible by using private endpoints in the same virtual network.

You must be able to access the contents of store1 by using the Azure Machine Learning SDK for Python. You must be able to preview the contents of store1 by using Azure Machine Learning studio.

You need to configure store1.

What should you do? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You create an Azure Databricks workspace and a linked Azure Machine Learning workspace.

You have the following Python code segment in the Azure Machine Learning workspace:

import mlflow

import mlflow.azureml

import azureml.mlflow

import azureml.core

from azureml.core import Workspace

subscription_id = 'subscription_id'

resourse_group = 'resource_group_name'

workspace_name = 'workspace_name'

ws = Workspace.get(name=workspace_name,

subscription_id=subscription_id,

resource_group=resource_group)

experimentName = "/Users/{user_name}/{experiment_folder}/{experiment_name}"

mlflow.set_experiment(experimentName)

uri = ws.get_mlflow_tracking_uri()

mlflow.set_tracking_uri(uri)

Instructions: For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

You create an Azure Machine Learning compute resource to train models. The compute resource is configured as follows:

• Minimum nodes: 2
• Maximum nodes: 4

You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:

• Minimum nodes: 0
• Maximum nodes: 8

You need to reconfigure the compute resource.

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

NOTE: Each correct selection is worth one point.