Question 46
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.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Question 47
You use Azure Machine Learning Studio to build a machine learning experiment.
You need to divide data into two distinct datasets.
Which module should you use?
You need to divide data into two distinct datasets.
Which module should you use?
Question 48
You need to implement source control for scripts in an Azure Machine Learning workspace. You use a terminal window in the Azure Machine Learning Notebook tab You must authenticate your Git account with SSH.
You need to generate a new SSH key.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them m the correct order.
You need to generate a new SSH key.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them m the correct order.
Question 49
You are creating a binary classification by using a two-class logistic regression model.
You need to evaluate the model results for imbalance.
Which evaluation metric should you use?
You need to evaluate the model results for imbalance.
Which evaluation metric should you use?
Question 50
You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameter.
In previous model training and tuning runs, many models showed similar performance.
You need to select an early termination policy that meets the following requirements:
* accounts for the performance of all previous runs when evaluating the current run
* avoids comparing the current run with only the best performing run to date Which two early termination policies should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
In previous model training and tuning runs, many models showed similar performance.
You need to select an early termination policy that meets the following requirements:
* accounts for the performance of all previous runs when evaluating the current run
* avoids comparing the current run with only the best performing run to date Which two early termination policies should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.