Microsoft DP-100 Practice Test - Questions Answers, Page 20

HOTSPOT

You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.

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

NOTE: Each correct selection is worth one point.

HOTSPOT

You publish a batch inferencing pipeline that will be used by a business application.

The application developers need to know which information should be submitted to and returned by the REST interface for the published pipeline.

You need to identify the information required in the REST request and returned as a response from the published pipeline.

Which values should you use in the REST request and to expect in the response? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

HOTSPOT

You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).

The remaining 1,000 rows represent class 1 (10 percent).

The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.

You need to configure the module.

Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.

NOTE: Each correct selection is worth one point.

HOTSPOT

You are running Python code interactively in a Conda environment. The environment includes all required Azure Machine Learning SDK and MLflow packages.

You must use MLflow to log metrics in an Azure Machine Learning experiment named mlflow-experiment.

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

NOTE: Each correct selection is worth one point.

DRAG DROP

You are creating a machine learning model that can predict the species of a penguin from its measurements. You have a file that contains measurements for three species of penguin in comma-delimited format.

The model must be optimized for area under the received operating characteristic curve performance metric, averaged for each class.

You need to use the Automated Machine Learning user interface in Azure Machine Learning studio to run an experiment and find the best performing model.

Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

HOTSPOT

You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter, training error, and validation errors.

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.

DRAG DROP

You create machine learning models by using Azure Machine Learning.

You plan to train and score models by using a variety of compute contexts. You also plan to create a new compute resource in Azure Machine Learning studio.

You need to select the appropriate compute types.

Which compute types should you select? To answer, drag the appropriate compute types to the correct requirements. Each compute type 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.

DRAG DROP

You are building an experiment using the Azure Machine Learning designer.

You split a dataset into training and testing sets. You select the Two-Class Boosted Decision Tree as the algorithm.

You need to determine the Area Under the Curve (AUC) of the model.

Which three modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

HOTSPOT

You register the following versions of a model.

You use the Azure ML Python SDK to run a training experiment. You use a variable named run to reference the experiment run.

After the run has been submitted and completed, you run the following code:

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

NOTE: Each correct selection is worth one point.