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Free Professional Machine Learning Engineer Exam Braindumps (page: 17)

Page 17 of 69
PDF with 285 Questions

While monitoring your model training’s GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the execution time of your input pipeline. What should you do?

  1. Increase the CPU load
  2. Add caching to the pipeline
  3. Increase the network bandwidth
  4. Add parallel interleave to the pipeline

Answer(s): D



Your data science team is training a PyTorch model for image classification based on a pre-trained RestNet model. You need to perform hyperparameter tuning to optimize for several parameters. What should you do?

  1. Convert the model to a Keras model, and run a Keras Tuner job.
  2. Run a hyperparameter tuning job on AI Platform using custom containers.
  3. Create a Kuberflow Pipelines instance, and run a hyperparameter tuning job on Katib.
  4. Convert the model to a TensorFlow model, and run a hyperparameter tuning job on AI Platform.

Answer(s): B



You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories. How should you configure the pipeline?

  1. Use the Cloud Natural Language API to obtain metadata to classify the incoming cases.
  2. Use AutoML Natural Language to build and test a classifier. Deploy the model as a REST API.
  3. Use BigQuery ML to build and test a logistic regression model to classify incoming requests. Use BigQuery ML to perform inference.
  4. Create a TensorFlow model using Google’s BERT pre-trained model. Build and test a classifier, and deploy the model using Vertex AI.

Answer(s): B



You need to quickly build and train a model to predict the sentiment of customer reviews with custom categories without writing code. You do not have enough data to train a model from scratch. The resulting model should have high predictive performance. Which service should you use?

  1. AutoML Natural Language
  2. Cloud Natural Language API
  3. AI Hub pre-made Jupyter Notebooks
  4. AI Platform Training built-in algorithms

Answer(s): A



Page 17 of 69



Post your Comments and Discuss Google Professional Machine Learning Engineer exam with other Community members:

Tina commented on April 09, 2024
Good questions
Anonymous
upvote

Kavah commented on September 29, 2021
Very responsive and cool support team.
UNITED KINGDOM
upvote