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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 191–195 (page: 40)

Updated On: 25-Oct-2026
Page 40 of 82
PDF with 402 Questions

You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predicts customer lifetime value over the next three years. You want to use the simplest approach to build the model.
What should you do?

  1. Create an Agent Platform Workbench notebook. Use IPython magic to run the CREATE MODEL statement to create a deep neural network (DNN) regressor model.
  2. Access BigQuery Studio in the Google Cloud console. Run the CREATE MODEL statement in the SQL editor to create a regression model using Agent Platform AutoML.
  3. Create an Agent Platform Workbench notebook. Use IPython magic to run the CREATE MODEL statement to create a regression model using Agent Platform AutoML.
  4. Access BigQuery Studio in the Google Cloud console. Run the CREATE MODEL statement in the SQL editor to create a deep neural network (DNN) regressor model.

Answer(s): B

Explanation:

This approach is the simplest for building a predictive model within BigQuery. Using AutoML regression allows you to leverage Google Cloud's automated machine learning capabilities, which simplifies the modeling process and requires minimal coding or manual configuration. Running the CREATE MODEL statement directly in BigQuery Studio provides an intuitive interface for managing and training your model using the existing historical customer data stored in BigQuery.



You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using AI Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness.
Which actions should you take? (Choose two.)

  1. Decrease the number of parallel trials.
  2. Decrease the range of floating-point values.
  3. Set the early stopping parameter to TRUE.
  4. Change the search algorithm from Bayesian search to random search.
  5. Decrease the maximum number of trials during subsequent training phases.

Answer(s): C,E



You developed a custom model by using Vertex AI to forecast the sales of your company’s products based on historical transactional data. You anticipate changes in the feature distributions and the correlations between the features in the near future. You also expect to receive a large volume of prediction requests. You plan to use Vertex AI Model Monitoring for drift detection and you want to minimize the cost.
What should you do?

  1. Use the features for monitoring. Set a monitoring-frequency value that is higher than the default.
  2. Use the features for monitoring. Set a prediction-sampling-rate value that is closer to 1 than 0.
  3. Use the features and the feature attributions for monitoring. Set a monitoring-frequency value that is lower than the default.
  4. Use the features and the feature attributions for monitoring. Set a prediction-sampling-rate value that is closer to 0 than 1.

Answer(s): D



You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving.
What should you do?

  1. Train a TensorFlow model on Vertex AI.
  2. Train a classification Vertex AutoML model.
  3. Run a logistic regression job on BigQuery ML.
  4. Use scikit-learn in Vertex AI Workbench user-managed notebooks with pandas library.

Answer(s): B



You recently developed a deep learning model. To test your new model, you trained it for a few epochs on a large dataset. You observe that the training and validation losses barely changed during the training run. You want to quickly debug your model.
What should you do first?

  1. Verify that your model can obtain a low loss on a small subset of the dataset
  2. Add handcrafted features to inject your domain knowledge into the model
  3. Use the Vertex AI hyperparameter tuning service to identify a better learning rate
  4. Use hardware accelerators and train your model for more epochs

Answer(s): A



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