Google Professional Machine Learning Engineer exam dumps

Google Professional Machine Learning Engineer practice question 257 of 522

Professional Machine Learning Engineer. Professional level, Google Cloud. Free question with the correct answer and a full explanation.

Google Professional Machine Learning Engineer Question 257

Select 4Google Cloud Platform

Your company wants to deploy a machine learning model to predict customer churn using a tabular dataset. The data is stored in BigQuery, and the model will be hosted on Kubernetes Engine for scalability. You decide to use AutoML Tables to build the model. Which of the following steps should you take to successfully implement this solution?

  1. A

    Use Google Cloud AutoML Tables to train the model directly on the BigQuery dataset and export the trained model.

  2. B

    Deploy the exported model to a Kubernetes Engine cluster using a containerized serving application.

  3. C

    Manually preprocess the BigQuery dataset and implement feature engineering in a custom script before using AutoML Tables.

  4. D

    Enable the BigQuery API and grant AutoML Tables read access to the dataset.

  5. E

    Configure a Kubernetes Engine cluster with autoscaling to handle variable traffic for model predictions.

Show answer and explanation

Correct answers: A, B, D, E

Explanation

To implement this solution, you start by using AutoML Tables to train the model directly on the BigQuery dataset, as AutoML handles preprocessing and feature engineering. The trained model is exported and deployed to Kubernetes Engine using a containerized application for serving predictions. Enabling the BigQuery API and granting read access are necessary for training, and autoscaling in Kubernetes Engine ensures the system remains scalable and efficient under changing workloads.

  • A. Correct.

    Correct: AutoML Tables can directly train on BigQuery datasets without the need for manual data extraction. This streamlines the workflow.

  • B. Correct.

    Correct: Once the model is trained using AutoML Tables, it can be exported and deployed to Kubernetes Engine for serving predictions.

  • C. Incorrect.

    Incorrect: AutoML Tables automatically handles preprocessing and feature engineering, so manual preprocessing is unnecessary.

  • D. Correct.

    Correct: AutoML Tables needs access to the BigQuery dataset to train the model. Enabling the BigQuery API and granting proper permissions is a required step.

  • E. Correct.

    Correct: Configuring Kubernetes Engine with autoscaling ensures the system can handle variable workloads efficiently when serving predictions.

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