Google Professional Machine Learning Engineer - Professional Machine Learning Engineer Exam
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Total 375 questions
Question #11 (Topic: Single Topic)
You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations AI to build, test, and
deploy your system. How should you develop recommendations that increase revenue while following best practices?
deploy your system. How should you develop recommendations that increase revenue while following best practices?
A. Use the ג€Other Products You May Likeג€ recommendation type to increase the click-through rate.
B. Use the ג€Frequently Bought Togetherג€ recommendation type to increase the shopping cart size for each order.
C. Import your user events and then your product catalog to make sure you have the highest quality event stream.
D. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.
Answer: B
Question #12 (Topic: Single Topic)
You are designing an architecture with a serverless ML system to enrich customer support tickets with informative metadata before they are routed to a support
agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions
when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:
Which endpoints should the Enrichment Cloud Functions call?
agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions
when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:
Which endpoints should the Enrichment Cloud Functions call?
A. 1 = AI Platform, 2 = AI Platform, 3 = AutoML Vision
B. 1 = AI Platform, 2 = AI Platform, 3 = AutoML Natural Language
C. 1 = AI Platform, 2 = AI Platform, 3 = Cloud Natural Language API
D. 1 = Cloud Natural Language API, 2 = AI Platform, 3 = Cloud Vision API
Answer: C
Question #13 (Topic: Single Topic)
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?
want the model to be resilient to overfitting. Which strategy should you use when retraining the model?
A. Apply a dropout parameter of 0.2, and decrease the learning rate by a factor of 10.
B. Apply a L2 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
C. Run a hyperparameter tuning job on AI Platform to optimize for the L2 regularization and dropout parameters.
D. 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: C
Question #14 (Topic: Single Topic)
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?
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?
A. Poor data quality
B. Lack of model retraining
C. Too few layers in the model for capturing information
D. Incorrect data split ratio during model training, evaluation, validation, and test
Answer: B
Question #15 (Topic: Single Topic)
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?
input data does not fit in memory. How should you create a dataset following Google-recommended best practices?
A. Create a tf.data.Dataset.prefetch transformation.
B. Convert the images to tf.Tensor objects, and then run Dataset.from_tensor_slices().
C. Convert the images to tf.Tensor objects, and then run tf.data.Dataset.from_tensors().
D. 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: D
