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.

Use the get_details_with_logs() method of the run object to display the experiment run logs.

View the logs for the experiment run in Azure Machine Learning studio.

Use the get_metrics() method of the run object to retrieve the experiment run logs.

Use the get_details_with_logs() method of the run object to display the experiment run logs.

View the log files for the experiment run in the experiment folder.

View the logs for the experiment run in Azure Machine Learning studio.

Use the get_output() method of the run object to retrieve the experiment run logs.

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?

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

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.

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

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

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

You are performing clustering by using the K-means algorithm.You need to define the possible termination conditions.Which three conditions can you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

Centroids do not change between iterations.

The residual sum of squares (RSS) falls below a threshold.

A fixed number of iterations is executed.

Centroids do not change between iterations.

The residual sum of squares (RSS) rises above a threshold.

The residual sum of squares (RSS) falls below a threshold.

A fixed number of iterations is executed.

The sum of distances between centroids reaches a maximum.

You are building a machine learning model for translating English language textual content into French language textual content.You need to build and train the machine learning model to learn the sequence of the textual content.Which type of neural network should you use?

Recurrent Neural Networks (RNNs)

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Generative Adversarial Networks (GANs)

You create a script that trains a convolutional neural network model over multiple epochs and logs the validation loss after each epoch. The script includes arguments for batch size and learning rate.You identify a set of batch size and learning rate values that you want to try.You need to use Azure Machine Learning to find the combination of batch size and learning rate that results in the model with the lowest validation loss.What should you do?

Run the script in an experiment based on a HyperDriveConfig object

Run the script in an experiment based on an AutoMLConfig object

Create a PythonScriptStep object for the script and run it in a pipeline

Use the Automated Machine Learning interface in Azure Machine Learning studio

Run the script in an experiment based on a ScriptRunConfig object

Run the script in an experiment based on a HyperDriveConfig object

You use the Azure Machine Learning Python SDK to define a pipeline to train a model.The data used to train the model is read from a folder in a datastore.You need to ensure the pipeline runs automatically whenever the data in the folder changes.What should you do?

Create a Schedule for the pipeline. Specify the datastore in the datastore property, and the folder containing the training data in the path_on_datastore property

Set the regenerate_outputs property of the pipeline to True

Create a ScheduleRecurrance object with a Frequency of auto. Use the object to create a Schedule for the pipeline

Create a PipelineParameter with a default value that references the location where the training data is stored

Create a Schedule for the pipeline. Specify the datastore in the datastore property, and the folder containing the training data in the path_on_datastore property