Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 281:

  DRAG DROP

  You have an Azure Machine Learning workspace named WS1.

  You plan to use WS1 to train two models named model1 and model2. For model1, you plan to use automated machine learning. For model2, you plan to use Azure Machine Learning designer.

  You need to determine the compute targets you should use to train each model. Your solution must ensure the following:

  1. The compute target for model1 supports auto-shutdown/auto-start based on a schedule.

  2. The compute target for model2 supports the use of low-priority Azure Virtual Machines.

  Which compute targets should you use? To answer, move the appropriate compute targets to the correct model. You may use each compute target once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  

• Question 282:

  You manage an Azure Machine Learning workspace named Workspace1.

  You plan to create a pipeline in the Azure Machine Learning Studio designer.

  The pipeline must include a custom component

  You need to ensure the custom component can be used in the pipeline.

  What should you do first?

  A. Add a linked service to Workspace1.
  B. Create a pipeline endpoint.
  C. Upload a json file to Workspace1.
  D. Upload a yaml file to Workspace1.
  E. Create a datastore.
  D. Upload a yaml file to Workspace1.

• Question 283:

  HOTSPOT

  You create a binary classification model using Azure Machine Learning Studio.

  You must use a Receiver Operating Characteristic (RO C) curve and an F1 score to evaluate the model.

  You need to create the required business metrics.

  How should you complete the experiment? To answer, select the appropriate options in the dialog box in the answer area;

  NOTE: Each correct selection is worth one point.

  

  

  

• Question 284:

  You create an MLflow model

  You must deploy the model to Azure Machine Learning for batch inference.

  You need to create the batch deployment.

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

  NOTE: Each correct selection is worth one point

  A. Compute target
  B. Kubernetes online endpoint
  C. Model files
  D. Online endpoint
  E. Environment
  A. Compute target
  C. Model files

• Question 285:

  You use Azure Machine Learning designer to load the following datasets into an experiment:

  Dataset1

  

  Dataset2

  

  You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.

  Solution: Use the Add Rows module.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

• Question 286:

  HOTSPOT

  You perform hyperparameter tuning with Azure Machine Learning.

  You create the following Python code:

  

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

  

  

• Question 287:

  You use Azure Machine Learning Designer lo load the following datasets into an experiment:

  Dataset1:

  

  Dataset2:

  

  You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.

  Solution: Use the Add Rows component.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 288:

  DRAG DROP

  You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are removed.

  Which three Azure Machine Learning Studio modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

  Select and Place:

  

  

  ---

  You can use the Clip Values module in Azure Machine Learning Studio, to identify and optionally replace data values that are above or below a specified threshold. This is useful when you want to remove outliers or replace them with a mean, a constant, or other substitute value.

  References:

  https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azuremachine-learning/

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clipvalues

• Question 289:

  You arc creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the Clean Missing Data module to handle the missing data.

  You need to select a data cleaning method.

  Which method should you use?

  A. Synthetic Minority
  B. Replace using Probabilistic PAC
  C. Replace using MICE
  D. Normalization
  B. Replace using Probabilistic PAC

• Question 290:

  You use the Azure Machine Learning SDK v2 for Python and notebooks to train a model. You use Python code to create a compute target, an environment, and a training script.

  You need to prepare information to submit a training job.

  Which class should you use?

  A. MLClient
  B. BuildContext
  C. EndpointConnection
  D. command
  D. command

  ---

  Explanation

  Build the training job

  Now that you have all the assets required to run your job, it's time to build it using the Azure Machine Learning Python SDK v2. For this, we'll be creating a command.

  An Azure Machine Learning command is a resource that specifies all the details needed to execute your training code in the cloud. These details include the inputs and outputs, type of hardware to use, software to install, and how to run your code. The command contains information to execute a single command.

  Configure the command

  You'll use the general purpose command to run the training script and perform your desired tasks. Create a Command object to specify the configuration details of your training job.

  The inputs for this command include the number of epochs, learning rate, momentum, and output directory.

  For the parameter values:

  provide the compute cluster cpu_compute_target = "cpu-cluster" that you created for running this command;

  provide the custom environment sklearn-env that you created for running the Azure Machine Learning job;

  configure the command line action itself--in this case, the command is python train_iris.py. You can access the inputs and outputs in the command via the ${{ ... }} notation; and configure the metadata such as the display name and experiment name; where an experiment is a container for all the iterations one does on a certain project. Note that all the jobs submitted under the same experiment name would be listed next to each other in Azure Machine Learning studio.

  https://github.com/MicrosoftDocs/azure-docs/blob/main/articles/machine-learning/how-to-train-scikit-learn.md