QUESTION: 13

You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation data. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

Apply a dropout parameter of 0.2, and decrease the learning rate by a factor of 10.
Apply a L2 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
Run a hyperparameter tuning job on AI Platform to optimize for the L2 regularization and dropout parameters.
Run a hyperparameter tuning job on AI Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.

Answer(s): C

Reveal Solution Next Question

QUESTION: 14

You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn’t changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?

Poor data quality
Lack of model retraining
Too few layers in the model for capturing information
Incorrect data split ratio during model training, evaluation, validation, and test

Answer(s): B

Reveal Solution Next Question

QUESTION: 15

You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?

Create a tf.data.Dataset.prefetch transformation.
Convert the images to tf.Tensor objects, and then run Dataset.from_tensor_slices().
Convert the images to tf.Tensor objects, and then run tf.data.Dataset.from_tensors().
Convert the images into TFRecords, store the images in Cloud Storage, and then use the tf.data API to read the images for training.

Answer(s): D

Reveal Solution Next Question

QUESTION: 16

You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your model’s features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

Classification
Reinforcement Learning
Recurrent Neural Networks (RNN)
Convolutional Neural Networks (CNN)

Answer(s): C

Reveal Solution Next Question

Free Professional Machine Learning Engineer Exam Braindumps (page: 4)

QUESTION: 13

QUESTION: 14

QUESTION: 15

QUESTION: 16