Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 331:

  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: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: A

  The following metrics are reported for evaluating regression models. When you compare models, they are ranked by the metric you select for evaluation.

  Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.

  Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.

  Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean.

  Relative squared error (RSE) similarly normalizes the total squared error of the predicted values by dividing by the total squared error of the actual values.

  Mean Zero One Error (MZOE) indicates whether the prediction was correct or not. In other words:

  ZeroOneLoss(x,y) = 1 when x!=y; otherwise 0.

  Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be

  used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.

  AUC.

  Reference:

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

• Question 332:

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

  

  You need to obtain the output from the pipeline execution. Where will you find the output?

  A. the digit_identification.py script

  B. the debug log

  C. the Activity Log in the Azure portal for the Machine Learning workspace

  D. the Inference Clusters tab in Machine Learning studio

  E. a file named parallel_run_step.txt located in the output folder

  Correct Answer: E

  output_action (str): How the output is to be organized. Currently supported values are 'append_row' and 'summary_only'.

  1.

  'append_row' ?All values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.

  2.

  'summary_only'

  Reference:

  https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/ azureml.contrib.pipeline.steps.parallelrunconfig

• Question 333:

  You use Azure Machine Learning designer to create a real-time service endpoint. You have a single Azure Machine Learning service compute resource.

  You train the model and prepare the real-time pipeline for deployment.

  You need to publish the inference pipeline as a web service.

  Which compute type should you use?

  A. a new Machine Learning Compute resource

  B. Azure Kubernetes Services

  C. HDInsight

  D. the existing Machine Learning Compute resource

  E. Azure Databricks

  Correct Answer: B

  Azure Kubernetes Service (AKS) can be used real-time inference.

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

• Question 334:

  You plan to run a script as an experiment using a Script Run Configuration. The script uses modules from the scipy library as well as several Python packages that are not typically installed in a default conda environment.

  You plan to run the experiment on your local workstation for small datasets and scale out the experiment by running it on more powerful remote compute clusters for larger datasets.

  You need to ensure that the experiment runs successfully on local and remote compute with the least administrative effort.

  What should you do?

  A. Do not specify an environment in the run configuration for the experiment. Run the experiment by using the default environment.

  B. Create a virtual machine (VM) with the required Python configuration and attach the VM as a compute target. Use this compute target for all experiment runs.

  C. Create and register an Environment that includes the required packages. Use this Environment for all experiment runs.

  D. Create a config.yaml file defining the conda packages that are required and save the file in the experiment folder.

  E. Always run the experiment with an Estimator by using the default packages.

  Correct Answer: C

  If you have an existing Conda environment on your local computer, then you can use the service to create an environment object. By using this strategy, you can reuse your local interactive environment on remote runs.

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

• Question 335:

  An organization creates and deploys a multi-class image classification deep learning model that uses a set of labeled photographs.

  The software engineering team reports there is a heavy inferencing load for the prediction web services during the summer. The production web service for the model fails to meet demand despite having a fully- utilized compute cluster where

  the web service is deployed.

  You need to improve performance of the image classification web service with minimal downtime and minimal administrative effort.

  What should you advise the IT Operations team to do?

  A. Create a new compute cluster by using larger VM sizes for the nodes, redeploy the web service to that cluster, and update the DNS registration for the service endpoint to point to the new cluster.

  B. Increase the node count of the compute cluster where the web service is deployed.

  C. Increase the minimum node count of the compute cluster where the web service is deployed.

  D. Increase the VM size of nodes in the compute cluster where the web service is deployed.

  Correct Answer: B

  The Azure Machine Learning SDK does not provide support scaling an AKS cluster. To scale the nodes in the cluster, use the UI for your AKS cluster in the Azure Machine Learning studio. You can only change the node count, not the VM size of the cluster.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-kubernetes

• Question 336:

  You create an Azure Machine Learning compute resource to train models. The compute resource is configured as follows:

  1.

  Minimum nodes: 2

  2.

  Maximum nodes: 4

  You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:

  1. Minimum nodes: 0

  2. Maximum nodes: 8

  You need to reconfigure the compute resource.

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

  NOTE: Each correct selection is worth one point.

  A. Use the Azure Machine Learning studio.

  B. Run the update method of the AmlCompute class in the Python SDK.

  C. Use the Azure portal.

  D. Use the Azure Machine Learning designer.

  E. Run the refresh_state() method of the BatchCompute class in the Python SDK.

  Correct Answer: ABC

  A: You can manage assets and resources in the Azure Machine Learning studio.

  B: The update(min_nodes=None, max_nodes=None, idle_seconds_before_scaledown=None) of the AmlCompute class updates the ScaleSettings for this AmlCompute target.

  C: To change the nodes in the cluster, use the UI for your cluster in the Azure portal.

  Reference: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute(class)

• Question 337:

  You create a new Azure subscription. No resources are provisioned in the subscription.

  You need to create an Azure Machine Learning workspace.

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

  NOTE: Each correct selection is worth one point.

  A. Run Python code that uses the Azure ML SDK library and calls the Workspace.create method with name, subscription_id, resource_group, and location parameters.

  B. Use an Azure Resource Management template that includes a Microsoft.MachineLearningServices/ workspaces resource and its dependencies.

  C. Use the Azure Command Line Interface (CLI) with the Azure Machine Learning extension to call the az group create function with --name and --location parameters, and then the az ml workspace create function, specifying 瓀 and 璯 parameters for the workspace name and resource group.

  D. Navigate to Azure Machine Learning studio and create a workspace.

  E. Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name, subscription_id, and resource_group parameters.

  Correct Answer: BCD

  B: You can use an Azure Resource Manager template to create a workspace for Azure Machine Learning.

  Example:

  {"type": "Microsoft.MachineLearningServices/workspaces", ...

  C: You can create a workspace for Azure Machine Learning with Azure CLI Install the machine learning extension.

  Create a resource group: az group create --name --location

  To create a new workspace where the services are automatically created, use the following command: az ml workspace create -w -g

  D: You can create and manage Azure Machine Learning workspaces in the Azure portal.

  1. Sign in to the Azure portal by using the credentials for your Azure subscription.

  2. In the upper-left corner of Azure portal, select + Create a resource.

  3. Use the search bar to find Machine Learning.

  4. Select Machine Learning.

  5. In the Machine Learning pane, select Create to begin.

  

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-workspace-template https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace-cli https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace

• Question 338:

  You create an Azure Machine Learning workspace. You are preparing a local Python environment on a laptop computer. You want to use the laptop to connect to the workspace and run experiments. You create the following config.json file.

  

  You must use the Azure Machine Learning SDK to interact with data and experiments in the workspace. You need to configure the config.json file to connect to the workspace from the Python environment.

  Which two additional parameters must you add to the config.json file in order to connect to the workspace? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. login

  B. resource_group

  C. subscription_id

  D. key

  E. region

  Correct Answer: BC

  To use the same workspace in multiple environments, create a JSON configuration file. The configuration file saves your subscription (subscription_id), resource (resource_group), and workspace name so that it can be easily loaded.

  The following sample shows how to create a workspace.

  from azureml.core import Workspace

  ws = Workspace.create(name='myworkspace',

  subscription_id='',

  resource_group='myresourcegroup',

  create_resource_group=True,

  location='eastus2'

  )

  Reference:

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace.workspace

• Question 339:

  You register a model that you plan to use in a batch inference pipeline.

  The batch inference pipeline must use a ParallelRunStep step to process files in a file dataset. The script has the ParallelRunStep step runs must process six input files each time the inferencing function is called.

  You need to configure the pipeline.

  Which configuration setting should you specify in the ParallelRunConfig object for the PrallelRunStep step?

  A. process_count_per_node= "6"

  B. node_count= "6"

  C. mini_batch_size= "6"

  D. error_threshold= "6"

  Correct Answer: B

  node_count is the number of nodes in the compute target used for running the ParallelRunStep.

  Incorrect Answers:

  A: process_count_per_node

  Number of processes executed on each node. (optional, default value is number of cores on node.)

  C: mini_batch_size

  For FileDataset input, this field is the number of files user script can process in one run() call. For TabularDataset input, this field is the approximate size of data the user script can process in one run() call.

  Example values are 1024, 1024KB, 10MB, and 1GB.

  D: error_threshold

  The number of record failures for TabularDataset and file failures for FileDataset that should be ignored during processing. If the error count goes above this value, then the job will be aborted.

  Reference:

  https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/ azureml.contrib.pipeline.steps.parallelrunconfig?view=azure-ml-py

• Question 340:

  You deploy a real-time inference service for a trained model.

  The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates.

  You need to implement a monitoring solution for the deployed model using minimal administrative effort.

  What should you do?

  A. View the explanations for the registered model in Azure ML studio.

  B. Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.

  C. View the log files generated by the experiment used to train the model.

  D. Create an ML Flow tracking URI that references the endpoint, and view the data logged by ML Flow.

  Correct Answer: B

  Configure logging with Azure Machine Learning studio

  You can also enable Azure Application Insights from Azure Machine Learning studio. When you're ready to deploy your model as a web service, use the following steps to enable Application Insights:

  1.

  Sign in to the studio at https://ml.azure.com.

  2.

  Go to Models and select the model you want to deploy.

  3.

  Select +Deploy.

  4.

  Populate the Deploy model form.

  5.

  Expand the Advanced menu.

  6.

  Select Enable Application Insights diagnostics and data collection.

  

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-enable-app-insights