PMLE - 4

What is one definition of a feature in machine learning?

• A place to store any data

• A value that is passed as input to a model

• A method of feature store

• A value that you receive from a model as an output

What is one-hot encoding?

• One-hot encoding is a process by which numeric variables are converted into a categorical form that could be provided to neural networks to do a better job in prediction.

• One-hot encoding is a process by which numeric variables are converted into a form that could be provided to neural networks to do a better job in prediction.

• One-hot encoding is a process by which only the hottest numeric variable is retained for use by the neural network.

• One-hot encoding is a process by which categorical variables are converted into a form that could be provided to neural networks to do a better job in prediction.

Which of these accurately describes the relationship between Apache Beam and Dataflow?

• Dataflow is the proprietary version of the Apache Beam API and the two are not compatible.

• They are the same.

• Apache Beam is the API for data pipeline building in Java or Python and Dataflow is the implementation and execution framework.

• No relationship.

1. True or False:

We can create many different kinds of feature crosses.

For example:

• [A X B]: a feature cross formed by multiplying the values of two features.

• [A x B x C x D x E]: a feature cross formed by multiplying the values of five features.

• [A x A]: a feature cross formed by squaring a single feature.

• True

• False

What does tf.Transform do during the training and serving phase?

• Provides a TensorFlow graph for preprocessing

• Provides computation over the entire dataset, including on both internal and external data sources

• Provides a transformation polynomial to train the data

• None of the options are correct.

The learning rate is a configurable hyperparameter used in the training of neural networks that has a small positive value, often in the range between _______

• < 0.0 and > 1.00.

• > 0.0 and < 1.00.

• 0.0 and 1.0.

• 1.0 and 3.0.

Which of the following is true?

• Larger batch sizes require larger learning rates.

• Larger batch sizes require smaller learning rates.

• Smaller batch sizes require smaller learning rates.

• Smaller batch sizes require larger learning rates.

Which of the following can make a huge difference in model quality?

• Decreasing the number of epochs.

• Increasing the learning rate.

• Increasing the training time.

• Setting hyperparameters to their optimal values for a given dataset.