Practice Free DP-100 Exam Online Questions

HOTSPOT

You must use in Azure Data Science Virtual Machine (DSVM) as a compute target.

You need to attach an existing DSVM to the workspace by using the Azure Machine Learning SDK for Python.

How should you complete the following code segment? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer:

HOTSPOT

You plan to implement a two-step pipeline by using the Azure Machine Learning SDK for Python.

The pipeline will pass temporary data from the first step to the second step.

You need to identify the class and the corresponding method that should be used in the second step to access temporary data generated by the first step in the pipeline.

Which class and method should you identify? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point

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Correct Answer:

You manage an Azure Machine Learning workspace. The development environment tor managing the workspace is configured to use Python SDK v2 in Azure Machine Learning Notebooks A Synapse Spark Compute is currently attached and uses system-assigned identity. You need to use Python code to update the Synapse Spark Compute 10 use a user-assigned identity.

Solution: Configure the IdentityConfiguration class with the appropriate identity type.

Does the solution meet the goal?

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HOTSPOT

You need to identify the methods for dividing the data according, to the testing requirements.

Which properties should you select? To answer, select the appropriate option-, m the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

Sampling

You are building a machine learning model for translating English language textual content into French language textual content.

You need to build and train the machine learning model to learn the sequence of the textual content.

Which type of neural network should you use?

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You run Azure Machine Learning training experiments. The training scripts directory contains 100 files that includes a file named. amlignore. The directory also contains subdirectories named. /outputs and./logs.

There are 20 files in the training scripts directory that must be excluded from the snapshot to the compute targets. You create a file named. gift ignore in the root of the directory. You add the names of the 20 files to the. gift ignore file. These 20 files continue to be copied to the compute targets.

You need to exclude the 20 files.

What should you do?

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You use the Azure Machine Learning Python SDK to create a batch inference pipeline.

You must publish the batch inference pipeline so that business groups in your organization can use the pipeline. Each business group must be able to specify a different location for the data that the pipeline submits to the model for scoring.

You need to publish the pipeline.

What should you do?

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DRAG DROP

You use Azure Machine Learning to deploy a model as a real-time web service.

You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.

Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

Box 1: init()

The entry script has only two required functions, init() and run(data). These functions are used to initialize the service at startup and run the model using request data passed in by a client. The rest of the script handles loading and running the model(s).

Box 2: run()

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

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 using Azure Machine Learning to run an experiment that trains a classification model.

You want to use Hyperdrive to find parameters that optimize the AUC metric for the model.

You configure a HyperDriveConfig for the experiment by running the following code:

You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.

You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.

Solution: Run the following code:

Does the solution meet the goal?

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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 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 AksWebservice instance.

Set the value of the auth_enabled property to False.

Set the value of the token_auth_enabled property to True.

Deploy the model to the service.

Does the solution meet the goal?

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