Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 211:

  You manage an Azure Machine Learning workspace named proj1.

  You plan to use assets defined in proj1 to create a pipeline in the Machine Learning studio designer.

  You need to set the Registry name filter to display only the list of assets defined in proj1.

  What should you set the Registry name filter to?

  A. proj1
  B. workspace
  C. azureml-meta
  D. azureml
  B. workspace

• Question 212:

  HOTSPOT

  You create an Azure Machine Learning workspace.

  You plan to write an Azure Machine Learning SDK for Python v2 script that logs an image for an experiment. The logged image must be available from the images tab in Azure Machine Learning Studio.

  You need to complete the script.

  Which code segments should you use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: mlflow

  Log an image to a PNG or JPEG file mlflow.log_artifact("Azure.png").

  The image is logged as an artifact and it appears in the Images tab in Azure Machine Learning Studio.

  Box 2: log_artifact

  https://learn.microsoft.com/en-us/azure/machine-learning/reference-migrate-sdk-v1-mlflow-tracking

• Question 213:

  You create an Azure Machine Learning workspace named ML-workspace. You also create an Azure Databricks workspace named DB-workspace. DB-workspace contains a cluster named DB-cluster.

  You must use DB-cluster to run experiments from notebooks that you import into DB-workspace.

  You need to use ML-workspace to track MLflow metrics and artifacts generated by experiments running on DB-cluster. The solution must minimize the need for custom code.

  What should you do?

  A. From DB-cluster, configure the Advanced Logging option.
  B. From DB-workspace, configure the Link Azure ML workspace option.
  C. From ML-workspace, create an attached compute.
  D. From ML-workspace, create a compute cluster.
  B. From DB-workspace, configure the Link Azure ML workspace option.

  ---

  Explanation

  Connect your Azure Databricks and Azure Machine Learning workspaces:

  Linking your ADB workspace to your Azure Machine Learning workspace enables you to track your experiment data in the Azure Machine Learning workspace.

  To link your ADB workspace to a new or existing Azure Machine Learning workspace 1. Sign in to Azure portal.

  2. Navigate to your ADB workspace's Overview page.

  3. Select the Link Azure Machine Learning workspace button on the bottom right.

  

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-databricks

• Question 214:

  HOTSPOT

  You have an Azure Machine Learning workspace named Workspace1.

  You plan to train an image object detection model by using Automated ML in Workspace1.

  You need to complete the provided Azure Machine Learning Python SDK v2 code to start an image object detection job.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 215:

  You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi- class image classification deep learning model that uses a set of labeled bird photographs collected by experts.

  You have 100,000 photographs of birds. All photographs use the JPG format and are stored in an Azure blob container in an Azure subscription.

  You need to access the bird photograph files in the Azure blob container from the Azure Machine Learning service workspace that will be used for deep learning model training. You must minimize data movement.

  What should you do?

  A. Create an Azure Data Lake store and move the bird photographs to the store.
  B. Create an Azure Cosmos DB database and attach the Azure Blob containing bird photographs storage to the database.
  C. Create and register a dataset by using TabularDataset class that references the Azure blob storage containing bird photographs.
  D. Register the Azure blob storage containing the bird photographs as a datastore in Azure Machine Learning service.
  E. Copy the bird photographs to the blob datastore that was created with your Azure Machine Learning service workspace.
  D. Register the Azure blob storage containing the bird photographs as a datastore in Azure Machine Learning service.

  ---

  Explanation

  We recommend creating a datastore for an Azure Blob container. When you create a workspace, an Azure blob container and an Azure file share are automatically registered to the workspace.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data

• Question 216:

  You train and register a machine learning model. You create a batch inference pipeline that uses the model to generate predictions from multiple data files.

  You must publish the batch inference pipeline as a service that can be scheduled to run every night.

  You need to select an appropriate compute target for the inference service.

  Which compute target should you use?

  A. Azure Machine Learning compute instance
  B. Azure Machine Learning compute cluster
  C. Azure Kubernetes Service (AKS)-based inference cluster
  D. Azure Container Instance (ACI) compute target
  B. Azure Machine Learning compute cluster

  ---

  Explanation

  Azure Machine Learning compute clusters is used for Batch inference. Run batch scoring on serverless compute. Supports normal and low-priority VMs. No support for real-time inference.

  https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target

• Question 217:

  DRAG DROP

  You need to define a modeling strategy for ad response.

  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: Implement a K-Means Clustering model

  Step 2: Use the cluster as a feature in a Decision jungle model.

  Decision jungles are non-parametric models, which can represent non-linear decision boundaries.

  Step 3: Use the raw score as a feature in a Score Matchbox Recommender model

  The goal of creating a recommendation system is to recommend one or more "items" to "users" of the system. Examples of an item could be a movie, restaurant, book, or song. A user could be a person, group of persons, or other entity with item preferences.

  Scenario:

  Ad response rated declined.

  Ad response models must be trained at the beginning of each event and applied during the sporting event.

  Market segmentation models must optimize for similar ad response history.

  Ad response models must support non-linear boundaries of features.

  References:

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

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

• Question 218:

  You create a pipeline in designer to train a model that predicts automobile prices.

  Because of non-linear relationships in the data, the pipeline calculates the natural log (Ln) of the prices in the training data, trains a model to predict this natural log of price value, and then calculates the exponential of the scored label to get the predicted price.

  The training pipeline is shown in the exhibit. (Click the Training pipeline tab.)

  Training pipeline

  

  You create a real-time inference pipeline from the training pipeline, as shown in the exhibit. (Click the Real-time pipeline tab.)

  Real-time pipeline

  

  You need to modify the inference pipeline to ensure that the web service returns the exponential of the scored label as the predicted automobile price and that client applications are not required to include a price value in the input values.

  Which three modifications must you make to the inference pipeline? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. Connect the output of the Apply SQL Transformation to the Web Service Output module.
  B. Replace the Web Service Input module with a data input that does not include the price column.
  C. Add a Select Columns module before the Score Model module to select all columns other than price.
  D. Replace the training dataset module with a data input that does not include the price column.
  E. Remove the Apply Math Operation module that replaces price with its natural log from the data flow.
  F. Remove the Apply SQL Transformation module from the data flow.
  A. Connect the output of the Apply SQL Transformation to the Web Service Output module.
  C. Add a Select Columns module before the Score Model module to select all columns other than price.
  E. Remove the Apply Math Operation module that replaces price with its natural log from the data flow.

• Question 219:

  HOTSPOT

  You have an Azure subscription that contains a resource group named rg-ml.

  You plan to create an Azure Machine Learning workspace named workspace1 in rg-ml by using Azure Machine Learning Python SDK v2.

  You need to ensure that the workspace is configured to prevent the collection of potentially sensitive data by Microsoft diagnostics.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 220:

  You manage an Azure Machine Learning workspace. The workspace includes an Azure Machine Learning Kubernetes compute target configured as an Azure Kubernetes Service (AKS) cluster named AKS1. AKS1 is configured to enable the targeting of different nodes to train workloads.

  You must run a command job on AKS1 by using the Azure ML Python SDK v2. The command job must select different types of compute nodes. The compute node types must be specified by using a command parameter.

  You need to configure the command parameter.

  Which parameter should you use?

  A. environment
  B. compute
  C. limits
  D. instance_type
  D. instance_type

  ---

  Explanation

  command

  Create a Command object which can be used inside dsl.pipeline as a function and can also be created as a standalone command job.

  Parameters include:

  instance_type

  Optional type of VM used as supported by the compute target.

  Incorrect:

  * environment the environment to use for this command

  * compute the name of the compute where the command job is executed( will not be used if the command is used as a component/function)

  https://learn.microsoft.com/en-us/python/api/azure-ai-ml/azure.ai.m