Designing and Implementing a Data Science Solution on Azure: DP-100

You are building a recurrent neural network to perform a binary classification.

You review the training loss, validation loss, training accuracy, and validation accuracy for each training epoch.

You need to analyze model performance.

You need to identify whether the classification model is overfitted.

Which of the following is correct?

A. The training loss stays constant and the validation loss stays on a constant value and close to the training loss value when training the model.

B. The training loss decreases while the validation loss increases when training the model.

C. The training loss stays constant and the validation loss decreases when training the model.

D. The training loss increases while the validation loss decreases when training the 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.

Solution: Run the following code:

Does the solution meet the goal?

A. Yes

B. No

You need to implement a model development strategy to determine a user's tendency to respond to an ad. Which technique should you use?

A. Use a Relative Expression Split module to partition the data based on centroid distance.

B. Use a Relative Expression Split module to partition the data based on distance travelled to the event.

C. Use a Split Rows module to partition the data based on distance travelled to the event.

D. Use a Split Rows module to partition the data based on centroid distance.