You use Azure Machine Learning Designer lo load the following datasets into an experiment:
Dataset1:

Dataset2:

You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Add Rows component.
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 creating a new experiment in Azure Learning learning Studio.
One class has a much smaller number of observations than the other classes in the training You need to select an appropriate data sampling strategy to compensate for the class imbalance.
Solution: You use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Does the solution meet the goal?

You need to record the row count as a metric named row_count that can be returned using the get_metrics method of the Run object after the experiment run completes. Which code should you use?

You are performing a classification task in Azure Machine Learning Studio.
You must prepare balanced testing and training samples based on a provided data set.
You need to split the data with a 0.75:0.25 ratio.
Which value should you use for each parameter? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

You plan to use Hyperdrive to optimize the hyperparameters selected when training a model. You create the following code to define options for the hyperparameter experiment


For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.