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 Pipelinefrom azureml.core.experiment import Experimentpipeline = 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.

Run the following code in a notebook:

Run the following code and monitor the console output from the PipelineRun object:

Run the following code in a notebook:

Use the Inference Clusters tab in Machine Learning Studio.

Use the Activity log in the Azure portal for the Machine Learning workspace.

Run the following code in a notebook:

Run the following code and monitor the console output from the PipelineRun object:

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?

service.update_deployment_state()

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?

Register the model, specifying the model_framework and model_framework_version properties.

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

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

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

Register the model, specifying the model_framework and model_framework_version properties.

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?

attached Azure Databricks cluster

Azure Kubernetes Service (AKS) inference cluster

Azure Machine Learning compute cluster

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?

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

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

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

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

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

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?

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

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.

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

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

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

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 new Machine Learning Compute resource

the existing Machine Learning Compute resource

You use the Azure Machine Learning Python SDK to define a pipeline that consists of multiple steps.When you run the pipeline, you observe that some steps do not run. The cached output from a previous run is used instead.You need to ensure that every step in the pipeline is run, even if the parameters and contents of the source directory have not changed since the previous run.What are two possible ways to achieve this goal? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

Set the regenerate_outputs property of the pipeline to True.

Set the allow_reuse property of each step in the pipeline to False.

Use a PipelineData object that references a datastore other than the default datastore.

Set the regenerate_outputs property of the pipeline to True.

Set the allow_reuse property of each step in the pipeline to False.

Restart the compute cluster where the pipeline experiment is configured to run.

Set the outputs property of each step in the pipeline to True.

You train a model and register it in your Azure Machine Learning workspace. You are ready to deploy the model as a real-time web service.You deploy the model to an Azure Kubernetes Service (AKS) inference cluster, but the deployment fails because an error occurs when the service runs the entry script that is associated with the model deployment.You need to debug the error by iteratively modifying the code and reloading the service, without requiring a re-deployment of the service for each code update.What should you do?

Create an Azure Container Instances (ACI) web service deployment configuration and deploy the model on ACI.

Modify the AKS service deployment configuration to enable application insights and re-deploy to AKS.

Create an Azure Container Instances (ACI) web service deployment configuration and deploy the model on ACI.

Add a breakpoint to the first line of the entry script and redeploy the service to AKS.

Create a local web service deployment configuration and deploy the model to a local Docker container.

Register a new version of the model and update the entry script to load the new version of the model from its registered path.

You use Azure Machine Learning designer to create a training pipeline for a regression model.You need to prepare the pipeline for deployment as an endpoint that generates predictions asynchronously for a dataset of input data values.What should you do?

Create a real-time inference pipeline from the training pipeline.

Create a batch inference pipeline from the training pipeline.

Create a real-time inference pipeline from the training pipeline.

Replace the dataset in the training pipeline with an Enter Data Manually module.

You retrain an existing model.You need to register the new version of a model while keeping the current version of the model in the registry.What should you do?

Register the model with the same name as the existing model.

Register a model with a different name from the existing model and a custom property named version with the value 2.

Register the model with the same name as the existing model.

Save the new model in the default datastore with the same name as the existing model. Do not register the new model.

Delete the existing model and register the new one with the same name.

You use the Azure Machine Learning SDK to run a training experiment that trains a classification model and calculates its accuracy metric.The model will be retrained each month as new data is available.You must register the model for use in a batch inference pipeline.You need to register the model and ensure that the models created by subsequent retraining experiments are registered only if their accuracy is higher than the currently registered model.What are two possible ways to achieve this goal? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

Specify the model framework version when registering the model, and only register subsequent models if this value is higher.

Specify a tag named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy tag value of the currently registered model.

Specify a different name for the model each time you register it.

Register the model with the same name each time regardless of accuracy, and always use the latest version of the model in the batch inferencing pipeline.

Specify the model framework version when registering the model, and only register subsequent models if this value is higher.

Specify a property named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy property value of the currently registered model.

Specify a tag named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy tag value of the currently registered model.

You are a data scientist working for a hotel booking website company. You use the Azure Machine Learning service to train a model that identifies fraudulent transactions.You must deploy the model as an Azure Machine Learning real-time web service using the Model.deploy method in the Azure Machine Learning SDK. The deployed web service must return real-time predictions of fraud based on transaction data input.You need to create the script that is specified as the entry_script parameter for the InferenceConfig class used to deploy the model.What should the entry script do?

Load the model and use it to predict labels from input data.

Register the model with appropriate tags and properties.

Create a Conda environment for the web service compute and install the necessary Python packages.

Load the model and use it to predict labels from input data.

Start a node on the inference cluster where the web service is deployed.

Specify the number of cores and the amount of memory required for the inference compute.

You develop and train a machine learning model to predict fraudulent transactions for a hotel booking website.Traffic to the site varies considerably. The site experiences heavy traffic on Monday and Friday and much lower traffic on other days. Holidays are also high web traffic days.You need to deploy the model as an Azure Machine Learning real-time web service endpoint on compute that can dynamically scale up and down to support demand.Which deployment compute option should you use?

attached virtual machine in a different region

attached Azure Databricks cluster

Azure Kubernetes Service (AKS) inference cluster

Azure Machine Learning Compute Instance

attached virtual machine in a different region

You use the designer to create a training pipeline for a classification model. The pipeline uses a dataset that includes the features and labels required for model training.You create a real-time inference pipeline from the training pipeline. You observe that the schema for the generated web service input is based on the dataset and includes the label column that the model predicts. Client applications that use the service must not be required to submit this value.You need to modify the inference pipeline to meet the requirement.What should you do?

Add a Select Columns in Dataset module to the inference pipeline after the dataset and use it to select all columns other than the label.

Delete the dataset from the training pipeline and recreate the real-time inference pipeline.

Delete the Web Service Input module from the inference pipeline.

Replace the dataset in the inference pipeline with an Enter Data Manually module that includes data for the feature columns but not the label column.

You use the Azure Machine Learning designer to create and run a training pipeline. You then create a real-time inference pipeline.You must deploy the real-time inference pipeline as a web service.What must you do before you deploy the real-time inference pipeline?

Create an Azure Machine Learning compute cluster.

Run the real-time inference pipeline.

Create a batch inference pipeline.

Create an Azure Machine Learning compute cluster.

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?

From DB-workspace, configure the Link Azure ML workspace option.

From DB-cluster, configure the Advanced Logging option.

From DB-workspace, configure the Link Azure ML workspace option.

From ML-workspace, create an attached compute.

From ML-workspace, create a compute cluster.

You have a Python script that executes a pipeline. The script includes the following code:from azureml.core import Experiment pipeline_run = Experiment(ws, 'pipeline_test').submit(pipeline)You want to test the pipeline before deploying the script.You need to display the pipeline run details written to the STDOUT output when the pipeline completes.Which code segment should you add to the test script?

pipeline_run.wait_for_completion(show_output=True)

pipeline_param = PipelineParameter(name="stdout",default_value="console")

You are a data scientist working for a bank and have used Azure ML to train and register a machine learning model that predicts whether a customer is likely to repay a loan.You want to understand how your model is making selections and must be sure that the model does not violate government regulations such as denying loans based on where an applicant lives.You need to determine the extent to which each feature in the customer data is influencing predictions.What should you do?

Use the interpretability package to generate an explainer for the model.

Enable data drift monitoring for the model and its training dataset.

Score the model against some test data with known label values and use the results to calculate a confusion matrix.

Use the Hyperdrive library to test the model with multiple hyperparameter values.

Use the interpretability package to generate an explainer for the model.

Add tags to the model registration indicating the names of the features in the training dataset.

You are determining if two sets of data are significantly different from one another by using Azure Machine Learning Studio.Estimated values in one set of data may be more than or less than reference values in the other set of data. You must produce a distribution that has a constant Type I error as a function of the correlation.You need to produce the distribution.Which type of distribution should you produce?

Paired t-test with a two-tail option

Unpaired t-test with a two-tail option

Unpaired t-test with a one-tail option

Paired t-test with a one-tail option

Paired t-test with a two-tail option

You are performing feature engineering on a dataset.You must add a feature named CityName and populate the column value with the text London.You need to add the new feature to the dataset.Which Azure Machine Learning Studio module should you use?

Extract N-Gram Features from Text

Microsoft DP-100 Practice Test 1 - Free Online Exam Questions 1-40

Question 1 / 40

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?

Azure Container Instance

Azure Kubernetes Service

Field Programmable Gate Array

Machine Learning Compute

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