Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 341:

  You train a machine learning model.

  You must deploy the model as a real-time inference service for testing. The service requires low CPU utilization and less than 48 MB of RAM. The compute target for the deployed service must initialize automatically while minimizing cost and administrative overhead.

  Which compute target should you use?

  A. Azure Container Instance (ACI)

  B. attached Azure Databricks cluster

  C. Azure Kubernetes Service (AKS) inference cluster

  D. Azure Machine Learning compute cluster

  Correct Answer: A

  Azure Container Instances (ACI) are suitable only for small models less than 1 GB in size. Use it for low-scale CPU-based workloads that require less than 48 GB of RAM. Note: Microsoft recommends using single-node Azure Kubernetes Service (AKS) clusters for dev-test of larger models.

  Reference: https://docs.microsoft.com/id-id/azure/machine-learning/how-to-deploy-and-where

• Question 342:

  You create an Azure Machine Learning workspace.

  You must create a custom role named DataScientist that meets the following requirements:

  1.

  Role members must not be able to delete the workspace.

  2.

  Role members must not be able to create, update, or delete compute resource in the workspace.

  3.

  Role members must not be able to add new users to the workspace.

  You need to create a JSON file for the DataScientist role in the Azure Machine Learning workspace.

  The custom role must enforce the restrictions specified by the IT Operations team.

  Which JSON code segment should you use?

  

  A. Option A

  B. Option B

  C. Option C

  D. Option D

  Correct Answer: A

  The following custom role can do everything in the workspace except for the following actions:

  1.

  It can't create or update a compute resource.

  2.

  It can't delete a compute resource.

  3.

  It can't add, delete, or alter role assignments.

  4.

  It can't delete the workspace.

  To create a custom role, first construct a role definition JSON file that specifies the permission and scope for the role. The following example defines a custom role named "Data Scientist Custom" scoped at a specific workspace level:

  data_scientist_custom_role.json :

  {

  "Name": "Data Scientist Custom",

  "IsCustom": true,

  "Description": "Can run experiment but can't create or delete compute.", "Actions": ["*"],

  "NotActions": [

  "Microsoft.MachineLearningServices/workspaces/*/delete", "Microsoft.MachineLearningServices/workspaces/write",

  "Microsoft.MachineLearningServices/workspaces/computes/*/write", "Microsoft.MachineLearningServices/workspaces/computes/*/delete", "Microsoft.Authorization/*/write"

  ],

  "AssignableScopes": [

  "/subscriptions//resourceGroups//providers/ Microsoft.MachineLearningServices/workspaces/" ]

  }

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-assign-roles

• Question 343:

  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.

  An IT department creates the following Azure resource groups and resources:

  

  The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace.

  You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.

  You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.

  Solution: Install the Azure ML SDK on the Surface Book. Run Python code to connect to the workspace and then run the training script as an experiment on local compute.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Need to attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace.

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

• Question 344:

  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.

  An IT department creates the following Azure resource groups and resources:

  

  The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace.

  You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.

  You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.

  Solution: Install the Azure ML SDK on the Surface Book. Run Python code to connect to the workspace. Run the training script as an experiment on the aks-cluster compute target.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Need to attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace.

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

• Question 345:

  You train and register a model in your Azure Machine Learning workspace.

  You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data.

  You need to create the inferencing script for the ParallelRunStep pipeline step.

  Which two functions should you include? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. run(mini_batch)

  B. main()

  C. batch()

  D. init()

  E. score(mini_batch)

  Correct Answer: AD

  Reference:

  https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learning- pipelines/parallel-run

• Question 346:

  You deploy a model as an Azure Machine Learning real-time web service using the following code.

  

  The deployment fails.

  You need to troubleshoot the deployment failure by determining the actions that were performed during deployment and identifying the specific action that failed.

  Which code segment should you run?

  A. service.get_logs()

  B. service.state

  C. service.serialize()

  D. service.update_deployment_state()

  Correct Answer: A

  You can print out detailed Docker engine log messages from the service object. You can view the log for ACI, AKS, and Local deployments. The following example demonstrates how to print the logs.

  # if you already have the service object handy print(service.get_logs())

  # if you only know the name of the service (note there might be multiple services with the same name but different version number) print(ws.webservices['mysvc'].get_logs())

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

• Question 347:

  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.

  An IT department creates the following Azure resource groups and resources:

  

  The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace.

  You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.

  You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.

  Solution: Attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace. Install the Azure ML SDK on the Surface Book and run Python code to connect to the workspace. Run the training script as an

  experiment on the mlvm remote compute resource.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: A

  Use the VM as a compute target.

  Note: A compute target is a designated compute resource/environment where you run your training script or host your service deployment. This location may be your local machine or a cloud-based compute resource.

  Reference:

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

• Question 348:

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

  You train the model by using PyTorch version 1.2.

  You need to ensure that the correct version of PyTorch can be identified for the inferencing environment when the model is deployed.

  What should you do?

  A. Save the model locally as a.pt file, and deploy the model as a local web service.

  B. Deploy the model on computer that is configured to use the default Azure Machine Learning conda environment.

  C. Register the model with a .pt file extension and the default version property.

  D. Register the model, specifying the model_framework and model_framework_version properties.

  Correct Answer: D

  framework_version: The PyTorch version to be used for executing training code.

  Reference: https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn.pytorch?view=azure-ml- py

• Question 349:

  You create a deep learning model for image recognition on Azure Machine Learning service using GPU- based training.

  You must deploy the model to a context that allows for real-time GPU-based inferencing.

  You need to configure compute resources for model inferencing.

  Which compute type should you use?

  A. Azure Container Instance

  B. Azure Kubernetes Service

  C. Field Programmable Gate Array

  D. Machine Learning Compute

  Correct Answer: B

  You can use Azure Machine Learning to deploy a GPU-enabled model as a web service. Deploying a model on Azure Kubernetes Service (AKS) is one option. The AKS cluster provides a GPU resource that is used by the model for inference.

  Inference, or model scoring, is the phase where the deployed model is used to make predictions. Using GPUs instead of CPUs offers performance advantages on highly parallelizable computation.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-inferencing-gpus

• Question 350:

  You create a batch inference pipeline by using the Azure ML SDK. You run the pipeline by using the following code:

  from azureml.pipeline.core import Pipeline

  from azureml.core.experiment import Experiment

  pipeline = Pipeline(workspace=ws, steps=[parallelrun_step]) pipeline_run = Experiment(ws, 'batch_pipeline').submit(pipeline)

  You need to monitor the progress of the pipeline execution.

  What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  

  A. Option A

  B. Option B

  C. Option C

  D. Option D

  E. Option E

  Correct Answer: DE

  A batch inference job can take a long time to finish. This example monitors progress by using a Jupyter widget. You can also manage the job's progress by using:

  1.

  Azure Machine Learning Studio.

  2.

  Console output from the PipelineRun object.

  from azureml.widgets import RunDetails RunDetails(pipeline_run).show() pipeline_run.wait_for_completion(show_output=True) Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-run-step#monitor-the- parallel-run-job