DP-100 Practice Exam Questions and Answers

You manage an Azure Machine Learning workspace.

You must log multiple metrics by using MLflow.

You need to maximize logging performance.

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

NOTE: Each correct selection is worth one point.

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 create a model to forecast weather conditions based on historical data.

You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.

Solution: Run the following code:

Does the solution meet the goal?

You are performing sentiment analysis using a CSV file that includes 12,000 customer reviews written in a short sentence format. You add the CSV file to Azure Machine Learning Studio and configure it as the starting point dataset of an experiment. You add the Extract N-Gram Features from Text module to the experiment to extract key phrases from the customer review column in the dataset.

You must create a new n-gram dictionary from the customer review text and set the maximum n-gram size to trigrams.

What should you select? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You train and register an Azure Machine Learning model

You plan to deploy the model to an online endpoint

You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.

Solution:

Create a managed online endpoint and set the value of its auth.mode parameter to aml.token. Deploy the model to the online endpoint.

Does the solution meet the goal?

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?

you create an Azure Machine learning workspace named workspace1. The workspace contains a Python SOK v2 notebook mat uses Mallow to correct model coaxing men’s anal arracks from your local computer.

Vou must reuse the notebook to run on Azure Machine I earning compute instance m workspace.

You need to comminute to log training and artifacts from your data science code.

What should you do?

You use the Azure Machine Learning service to create a tabular dataset named training.data. You plan to use this dataset in a training script.

You create a variable that references the dataset using the following code:

training_ds = workspace.datasets.get("training_data")

You define an estimator to run the script.

You need to set the correct property of the estimator to ensure that your script can access the training.data dataset

Which property should you set?

A)

B)

C)

D)

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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.

You must run the script as an Azure ML experiment on a compute cluster named aml-compute.

You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.

Solution: Run the following code:

Does the solution meet the goal?

You create a training pipeline by using the Azure Machine Learning designer. You need to load data into a machine learning pipeline by using the Import Data component. Which two data sources could you use? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point

You use the Azure Machine Learning SDK in a notebook to run an experiment using a script file in an experiment folder.

The experiment fails.

You need to troubleshoot the failed experiment.

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