Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 511:

  You create a training pipeline by using the Azure Machine Learning designer.

  You need to load data into a machine learning pipeline by using the Import Data component.

  Which two data sources could you use? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point

  A. Azure Blob storage container through a registered datastore
  B. Azure SQL Database
  C. URL via HTTP
  D. Azure Data Lake Storage Gen2
  E. Registered dataset
  A. Azure Blob storage container through a registered datastore
  E. Registered dataset

• Question 512:

  DRAG DROP

  You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.

  You must register datastores in workspace1 for Azure Blob storage and Azure Files storage to meet the following requirements:

  Azure Active Directory (Azure AD) authentication must be used for access to storage when possible.

  Credentials and secrets stored in workspace1 must be valid for a specified time period when accessing Azure Files storage.

  You need to configure a security access method used to register the Azure Blob and Azure Files storage in workspace1.

  Which security access method should you configure? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  

  ---

  Explanation:

  Box 1: Identity-based access

  Azure Active Directory (Azure AD) authentication must be used for access to storage when possible.

  To authenticate your access to the underlying storage service, you can provide either your account key, shared access signatures (SAS) tokens, or service principal according to the datastore type you want to create.

  Managed identity: When using the Azure Machine Learning SDK v2 on a compute instance or on an Azure Virtual Machine, you can use a managed identity for Azure. This workflow allows the VM to connect to the workspace using the managed identity, without storing credentials in Python code or prompting the user to authenticate. Azure Machine Learning compute clusters can also be configured to use a managed identity to access the workspace when training models.

  Box 2: Shared Access Signature (SAS)

  Credentials and secrets stored in workspace1 must be valid for a specified time period when accessing Azure Files storage.

  A shared access signature (SAS) provides secure delegated access to resources in your storage account. With a SAS, you have granular control over how a client can access your data. For example:

  What resources the client may access.

  What permissions they have to those resources.

  How long the SAS is valid.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-authentication

  https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-connect-data-ui

• Question 513:

  You manage an Azure Machine Learning workspace that includes a batch endpoint.

  You plan to deploy a model to the batch endpoint.

  You need to configure compute for the deployment.

  Which compute should you use?

  A. Remote VM
  B. AmlCompute instance
  C. Azure Batch
  D. Kubernetes cluster
  D. Kubernetes cluster

• Question 514:

  HOTSPOT

  You are working on a classification task. You have a dataset indicating whether a student would like to play soccer and associated attributes. The dataset includes the following columns:

  

  You need to classify variables by type.

  Which variable should you add to each category? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  References:

  https://www.edureka.co/blog/classification-algorithms/

• Question 515:

  You manage an Azure Machine Learning workspace.

  You configure a standalone Apache Spark job in the workspace by using Python SDK v2.

  You need to create the job by using an Azure Machine Learning serverless Spark compute with input, output, and user identity.

  Which two classes should you configure? (Choose two.)

  A. Spark
  B. SynapseSparkCompute
  C. MLCIient
  D. IdentityConfiguration
  A. Spark
  D. IdentityConfiguration

• Question 516:

  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 creating a model to predict the price of a student's artwork depending on the following variables: the student's length of education, degree type, and art form.

  You start by creating a linear regression model.

  You need to evaluate the linear regression model.

  Solution: Use the following metrics: Accuracy, Precision, Recall, F1 score, and AUC.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Those are metrics for evaluating classification models, instead use: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.

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

• Question 517:

  HOTSPOT

  You develop a chat flow in an Azure AI Foundry project.

  You plan to include a Jinja language-based prompt template in the flow.

  You need to complete the provided template to display a list of inputs and outputs included in the flow.

  How should you complete the provided template? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

• Question 518:

  You manage an Azure Machine Learning workspace.

  You need to define an environment from a Docker image by using the Azure Machine Learning Python SDK v2.

  Which parameter should you use?

  A. properties
  B. image
  C. build
  D. conda_file
  B. image

  ---

  Explanation

  Create an environment from a Docker image

  To define an environment from a Docker image, provide the image URI of the image hosted in a registry such as Docker Hub or Azure Container Registry.

  The following example is a YAML specification file for an environment defined from a Docker image. An image from the official PyTorch repository on Docker Hub is specified via the image property in the YAML file.

  $schema:

  https://azuremlschemas.azureedge.net/latest/environment.schema.jsonname:docker-image-exampleimage:pytorch/pytorch:latestdescription:

  Environment created from a Docker image.

  To create the environment:

  az ml environment create --file assets/environment/docker-image.yml

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2

• Question 519:

  DRAG DROP

  You need to define an evaluation strategy for the crowd sentiment models.

  Which three 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: Define a cross-entropy function activation

  When using a neural network to perform classification and prediction, it is usually better to use cross-entropy error than classification error, and somewhat better to use cross-entropy error than mean squared error to evaluate the quality of the neural network.

  Step 2: Add cost functions for each target state.

  Step 3: Evaluated the distance error metric.

  References:

  https://www.analyticsvidhya.com/blog/2018/04/fundamentals-deep-learning-regularization-techniques/

• Question 520:

  You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and PyTorch.

  You need to select a pre-configured DSVM to support the frameworks.

  What should you create?

  A. Data Science Virtual Machine for Windows 2012
  B. Data Science Virtual Machine for Linux (CentOS)
  C. Geo AI Data Science Virtual Machine with ArcGIS
  D. Data Science Virtual Machine for Windows 2016
  E. Data Science Virtual Machine for Linux (Ubuntu)
  E. Data Science Virtual Machine for Linux (Ubuntu)

  ---

  Explanation

  Caffe2 and PyTorch 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. Only the DSVM on Ubuntu is preconfigured for Caffe2 and PyTorch.

  Incorrect Answers:

  D: Caffe2 and PytOCH are only supported in the Data Science Virtual Machine for Linux.

  References:

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