Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 391:

  DRAG DROP

  You create an Azure Machine Learning workspace and a new Azure DevOps organization. You register a model in the workspace and deploy the model to the target environment.

  All new versions of the model registered in the workspace must automatically be deployed to the target environment.

  You need to configure Azure Pipelines to deploy the model.

  Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

  Select and Place:

  

  

  ---

  Step 1: Create an Azure DevOps project

  Step 2: Create a release pipeline 1. Sign in to your Azure DevOps organization and navigate to your project.

  2. Go to Pipelines, and then select New pipeline.

  Step 3: Install the Machine Learning extension for Azure Pipelines You must install and configure the Azure CLI and ML extension.

  Step 4: Create a service connection

  How to set up your service connection

  

  Select AzureMLWorkspace for the scope level, then fill in the following subsequent parameters.

  

  Note: How to enable model triggering in a release pipeline 1. Go to your release pipeline and add a new artifact. Click on AzureML Model artifact then select the appropriate AzureML service connection and select from the available models in your workspace.

  2. Enable the deployment trigger on your model artifact as shown here. Every time a new version of that model is registered, a release pipeline will be triggered.

  https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.vss-services-azureml

  https://docs.microsoft.com/en-us/azure/devops/pipelines/targets/azure-machine-learning

• Question 392:

  You are analyzing a dataset by using Azure Machine Learning Studio.

  You need to generate a statistical summary that contains the p-value and the unique count for each feature column.

  Which two modules can you use? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Computer Linear Correlation
  B. Export Count Table
  C. Execute Python Script
  D. Convert to Indicator Values
  E. Summarize Data
  C. Execute Python Script
  E. Summarize Data

• Question 393:

  You manage an Azure Machine Learning workspace.

  The Pylhon scrip! named scriptpy reads an argument named training_data.

  The trainlng.data argument specifies the path to the training data in a file named datasetl.csv.

  You plan to run the scriptpy Python script as a command job that trains a machine learning model.

  You need to provide the command to pass the path for the datasct as a parameter value when you submit the script as a training job.

  Solution: python train.py --training_data training_data

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  
  

• Question 394:

  HOTSPOT

  You create an Azure Machine Learning workspace named workspace1. You assign a custom role to a user of workspace1.

  The custom role has the following JSON definition:

  

  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.

  

  

  ---

  Box 1: No

  The actions listed in NotActions are prohibited.

  If the roles include Actions that have a wildcard (*), the effective permissions are computed by subtracting the NotActions from the allowed Actions.

  Box 2: No

  Deleting compute resources in the workspace is in the NotActions list.

  Box 3: Yes

  Writing metrics is not listed in NotActions.

  https://docs.microsoft.com/en-us/azure/role-based-access-control/overview#how-azure-rbac-determines-if-a-user-has-access-to-a-resource

• Question 395:

  You plan to build a team data science environment. Data for training models in machine learning pipelines will be over 20 GB in size.

  You have the following requirements:

  1. Models must be built using Caffe2 or Chainer frameworks.

  2. Data scientists must be able to use a data science environment to build the machine learning pipelines and train models on their personal devices in both connected and disconnected network environments.

  Personal devices must support updating machine learning pipelines when connected to a network.

  You need to select a data science environment.

  Which environment should you use?

  A. Azure Machine Learning Service
  B. Azure Machine Learning Studio
  C. Azure Databricks
  D. Azure Kubernetes Service (AKS)
  A. Azure Machine Learning Service

  ---

  Explanation

  The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft's Azure cloud built specifically for doing data science. Caffe2 and Chainer are supported by DSVM.

  DSVM integrates with Azure Machine Learning.

  Incorrect Answers:

  B: Use Machine Learning Studio when you want to experiment with machine learning models quickly and easily, and the built-in machine learning algorithms are sufficient for your solutions.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

• Question 396:

  HOTSPOT

  You create an Azure Machine learning workspace and load a Python training script named tram.py in the src subfolder. The dataset used to train your model is available locally. You run the following script to tram the model:

  

  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

  

  

• Question 397:

  You create an Azure Machine Learning workspace. You use Azure Machine Learning designer to create a pipeline within the workspace.

  You need to submit a pipeline run from the designer.

  What should you do first?

  A. Create an experiment.
  B. Create an attached compute resource.
  C. Create a compute cluster.
  D. Select a model.
  A. Create an experiment.

  ---

  Explanation

  https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-train-score

• Question 398:

  You are preparing to train a regression model via automated machine learning. The data available to you has features with missing values, as well as categorical features with little discrete values.

  You want to make sure that automated machine learning is configured as follows:

  missing values must be automatically imputed. categorical features must be encoded as part of the training task.

  Which of the following actions should you take?

  A. You should make use of the featurization parameter with the 'auto' value pair.
  B. You should make use of the featurization parameter with the 'off' value pair.
  C. You should make use of the featurization parameter with the 'on' value pair.
  D. You should make use of the featurization parameter with the 'FeaturizationConfig' value pair.
  A. You should make use of the featurization parameter with the 'auto' value pair.

  ---

  Explanation

  Featurization str or FeaturizationConfig

  Values: 'auto' / 'off' / FeaturizationConfig

  Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used.

  Column type is automatically detected. Based on the detected column type preprocessing/featurization is done as follows:

  Categorical: Target encoding, one hot encoding, drop high cardinality categories, impute missing values.

  Numeric: Impute missing values, cluster distance, weight of evidence.

  DateTime: Several features such as day, seconds, minutes, hours etc.

  Text: Bag of words, pre-trained Word embedding, text target encoding.

  https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig

• Question 399:

  DRAG DROP

  You have been tasked with moving data into Azure Blob Storage for the purpose of supporting Azure Machine Learning.

  Which of the following can be used to complete your task? Answer by dragging the correct options from the list to the answer area.

  Select and Place:

  

  

  ---

  You can move data to and from Azure Blob storage using different technologies:

  Azure Storage-Explorer

  AzCopy

  Python

  SSIS Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/move-azure-blob

• Question 400:

  You manage an Azure Machine Learning workspace.

  You plan to import data from Azure Data Lake Storage Gen2.

  You need to build a URI that represents the storage location.

  Which protocol should you use?

  A. https
  B. adl
  C. abfss
  D. wasbs
  C. abfss

  ---

  Explanation

  https://learn.microsoft.com/en-us/azure/machine-learning/concept-data?view=azureml-api-2#uri