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DP-100 Exam Dumps - Designing and Implementing a Data Science Solution on Azure

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Question # 73

You arc I mating a deep learning model to identify cats and dogs. You have 25,000 color images.

You must meet the following requirements:

• Reduce the number of training epochs.

• Reduce the size of the neural network.

• Reduce over-fitting of the neural network.

You need to select the image modification values.

Which value should you use? To answer, select the appropriate Options in the answer area.

NOTE: Each correct selection is worth one point.

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Question # 74

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?

A.

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

B.

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

C.

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

D.

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

E.

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

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Question # 75

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?

A.

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

B.

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

C.

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

D.

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

E.

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

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