Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 51:

  You manage an Azure AI Foundry project. You plan to create a vector index for a RAG solution. You need to build the index remotely by using a script.

  Which two data sources can you use? (Choose two.) Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. OneLake in Microsoft Fabric
  B. GitHub repositories
  C. Azure Al Search
  D. Local files and folders
  C. Azure Al Search
  D. Local files and folders

• Question 52:

  HOTSPOT

  A biomedical research company plans to enroll people in an experimental medical treatment trial.

  You create and train a binary classification model to support selection and admission of patients to the trial. The model includes the following features: Age, Gender, and Ethnicity.

  The model returns different performance metrics for people from different ethnic groups.

  You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature.

  Which technique and constraint should you use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

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  Box 1: Grid Search

  Fairlearn open-source package provides postprocessing and reduction unfairness mitigation algorithms: ExponentiatedGradient, GridSearch, and ThresholdOptimizer.

  Note: The Fairlearn open-source package provides postprocessing and reduction unfairness mitigation algorithms types:

  1. Reduction: These algorithms take a standard black-box machine learning estimator (e.g., a LightGBM model) and generate a set of retrained models using a sequence of re-weighted training datasets.

  2. Post-processing: These algorithms take an existing classifier and the sensitive feature as input.

  Box 2: Demographic parity

  The Fairlearn open-source package supports the following types of parity constraints: Demographic parity, Equalized odds, Equal opportunity, and Bounded group loss.

  https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml

• Question 53:

  You need to implement a Data Science Virtual Machine (DSVM) that supports the Caffe2 deep learning framework.

  Which of the following DSVM should you create?

  A. Windows Server 2012 DSVM
  B. Windows Server 2016 DSVM
  C. Ubuntu 16.04 DSVM
  D. CentOS 7.4 DSVM
  C. Ubuntu 16.04 DSVM

  ---

  Explanation

  Caffe2 is supported by Data Science Virtual Machine for Linux.

  Microsoft offers Linux editions of the DSVM on Ubuntu 16.04 LTS and CentOS 7.4.

  However, only the DSVM on Ubuntu is preconfigured for Caffe2.

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

• Question 54:

  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 have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.

  You plan to add a new Jupyter kernel that will be accessible from the same terminal session.

  You need to perform the task that must be completed before you can add the new kernel.

  Solution: Create an environment.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

• Question 55:

  DRAG DROP

  You create a multi-class image classification deep learning model.

  The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images, and retrain the model.

  You need to use the Azure Machine Learning SDK to configure the schedule for the pipeline.

  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: Publish the pipeline.

  To schedule a pipeline, you'll need a reference to your workspace, the identifier of your published pipeline, and the name of the experiment in which you wish to create the schedule.

  Step 2: Retrieve the pipeline ID.

  Needed for the schedule.

  Step 3: Create a ScheduleRecurrence..

  To run a pipeline on a recurring basis, you'll create a schedule. A Schedule associates a pipeline, an experiment, and a trigger.

  First create a schedule.

  Example: Create a Schedule that begins a run every 15 minutes:

  recurrence = ScheduleRecurrence(frequency="Minute", interval=15)

  Step 4: Define an Azure Machine Learning pipeline schedule..

  Example, continued:

  recurring_schedule = Schedule.create(ws, name="MyRecurringSchedule", description="Based on time", pipeline_id=pipeline_id, experiment_name=experiment_name, recurrence=recurrence)

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-schedule-pipelines

• Question 56:

  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 have the following Azure subscriptions and Azure Machine Learning service workspaces:

  

  You need to obtain a reference to the ml-project workspace.

  Solution: Run the following Python code:

  

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 57:

  HOTSPOT

  You need to identify the methods for dividing the data according to the testing requirements.

  Which properties should you select? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Scenario: Testing

  You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

  Box 1: Assign to folds

  Use Assign to folds option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.

  Not Head: Use Head mode to get only the first n rows. This option is useful if you want to test a pipeline on a small number of rows, and don't need the data to be balanced or sampled in any way.

  Not Sampling: The Sampling option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.

  Box 2: Partition evenly

  Specify the partitioner method: Indicate how you want data to be apportioned to each partition, using these options: Partition evenly: Use this option to place an equal number of rows in each partition. To specify the number of output partitions, type a whole number in the Specify number of folds to split evenly into text box.

  https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/partition-and-sample

• Question 58:

  You are developing a data science workspace that uses an Azure Machine Learning service.

  You need to select a compute target to deploy the workspace.

  What should you use?

  A. Azure Data Lake Analytics
  B. Azure Databricks
  C. Azure Container Service
  D. Apache Spark for HDInsight
  B. Azure Databricks

• Question 59:

  HOTSPOT

  You create an Azure Data Lake Storage Gen2 storage account named storage1 containing a file system named fs1 and a folder named folder1.

  The contents of folder1 must be accessible from jobs on compute targets in the Azure Machine Learning workspace.

  You need to construct a URI to reference folder1.

  How should you construct the URI? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

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  Explanation:

  Box 1: abfss

  The URI syntax for Data Lake Storage Gen2 is dependent on whether or not your storage account is set up to have Data Lake Storage Gen2 as the default file system.

  If the Data Lake Storage Gen2 capable account you wish to address is not set as the default file system during account creation, then the shorthand URI syntax is:

  abfs[s]1://<file_system>2@<account_name>3.dfs.core.windows.net/<path>4/<file_name>5

  Scheme identifier: The abfs protocol is used as the scheme identifier. If you add an 's' at the end (abfss) then the ABFS Hadoop client driver will ALWAYS use Transport Layer Security (TLS) irrespective of the authentication method chosen. If you choose OAuth as your authentication then the client driver will always use TLS even if you specify 'abfs' instead of 'abfss' because OAuth solely relies on the TLS layer. Finally, if you choose to use the older method of storage account key, then the client driver will interpret 'abfs' to mean that you do not want to use TLS.

  File system: The parent location that holds the files and folders. This is the same as Containers in the Azure Storage Blobs service.

  Account name: The name given to your storage account during creation.

  Paths: A forward slash delimited (/) representation of the directory structure.

  File name: The name of the individual file. This parameter is optional if you are addressing a directory.

  Box 2: [email protected]/folder1/

  https://learn.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-introduction-abfs-uri

• Question 60:

  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 using Azure Machine Learning to run an experiment that trains a classification model.

  You want to use Hyperdrive to find parameters that optimize the AUC metric for the model. You configure a HyperDriveConfig for the experiment by running the following code:

  

  You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.

  You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

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  Explanation

  Python printing/logging example:

  logging.info(message) Destination: Driver logs, Azure Machine Learning designer

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines