Professional Machine Learning Engineer Practice Test

Professional Machine Learning Engineer Practice Test practice questions, page 14 of 15, with answer review and explanations.

Question 66

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.

Question 67

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.

Question 68

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

Question 69

You need to build an ML model for a social media application to predict whether a user’s submitted profile photo meets the requirements. The application will inform the user if the picture meets the requirements. How should you build a model to ensure that the application does not falsely accept a non-compliant picture?

  1. Use AutoML to optimize the model’s recall in order to minimize false negatives.
  2. Use AutoML to optimize the model’s F1 score in order to balance the accuracy of false positives and false negatives.
  3. Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that meet the profile photo requirements.
  4. Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that do not meet the profile photo requirements.

Question 70

You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team usually takes a few weeks or months to iterate on a new version of a model. You were recently asked to review your team’s spending. How should you reduce your Google Cloud compute costs without impacting the model’s performance?

  1. Use AI Platform to run distributed training jobs with checkpoints.
  2. Use AI Platform to run distributed training jobs without checkpoints.
  3. Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with checkpoints.
  4. Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs without checkpoints.