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

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.

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You train and register a machine learning model.

You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model.

You need to deploy the web service.

Solution:

Create an AciWebservice instance.

Set the value of the ssl_enabled property to True.

Deploy the model to the service.

Does the solution meet the goal?

You manage an Azure Machine Learning workspace.

You choose the urijolder data type as an output of a pipeline component.

You need to define the data access mode that is supported by your configuration.

Which mode should you define?

You use Azure Machine Learning to train and register a model.

You must deploy the model into production as a real-time web service to an inference cluster named service-compute that the IT department has created in the Azure Machine Learning workspace.

Client applications consuming the deployed web service must be authenticated based on their Azure Active Directory service principal.

You need to write a script that uses the Azure Machine Learning SDK to deploy the model. The necessary modules have been imported.

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

NOTE: Each correct selection is worth one point.

You deploy a model in Azure Container Instance.

You must use the Azure Machine Learning SDK to call the model API.

You need to invoke the deployed model using native SDK classes and methods.

How should you complete the command? To answer, select the appropriate options in the answer areas.

NOTE: Each correct selection is worth one point.

You manage an Azure Al Foundry project.

You plan to fine-tune a base model by using pre-uploaded training and validation data. You must specify a hyperparameter to ensure the job is reproducible.

You need to submit the fine-tuning training job.

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

NOTE: Each correct selection is worth one point.

You manage an Azure Machine Learning workspace. You submit a training job with the Azure Machine Learning Python SDK v2. You must use MLflow to log metrics, model parameters, and model artifacts automatically when training a model.

You start by writing the following code segment:

For each of the following statements, select Yes If the statement is true. Otherwise, select No.

You are using the Azure Machine Learning Service to automate hyperparameter exploration of your neural network classification model.

You must define the hyperparameter space to automatically tune hyperparameters using random sampling according to following requirements:

The learning rate must be selected from a normal distribution with a mean value of 10 and a standard deviation of 3.

Batch size must be 16, 32 and 64.

Keep probability must be a value selected from a uniform distribution between the range of 0.05 and 0.1.

You need to use the param_sampling method of the Python API for the Azure Machine Learning Service.

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

NOTE: Each correct selection is worth one point.

You have an Azure Machine Learning workspace and a data source file ./data/cc_data.csv in the local storage.

You plan to use Azure Machine Learning Python SDK v2 to store the content of the cc.data.csv file in a data asset named cc_data_asset in the workspace.

You write code to connect to the workspace and import all required libraries.

You need to complete the remaining code to ensure it will result in the cc_data_asset that contains the data from cc_data.csv.

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

NOTE: Each correct selection is worth one point.