Google Professional Machine Learning Engineer exam dumps

Google Professional Machine Learning Engineer practice question 250 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 250

Select 2Google Cloud Platform

You are developing a machine learning workflow for a predictive model and need to train a custom model using TensorFlow with distributed training. The organization requires the solution to be scalable, cost-effective, and integrated with Google Cloud services for seamless deployment. Which tools should you use to train the model?

  1. A

    Vertex AI custom training with preemptible VMs

  2. B

    Kubeflow Pipelines with TensorFlow and GKE

  3. C

    Vertex AI AutoML for tabular data

  4. D

    Local Jupyter Notebooks without cloud integration

  5. E

    BigQuery ML for training the model

Show answer and explanation

Correct answers: A, B

Explanation

To meet the requirements of scalable, cost-effective, and Google Cloud-integrated training, Vertex AI custom training with preemptible VMs and Kubeflow Pipelines with TensorFlow and GKE are the best choices. Vertex AI supports distributed training with TensorFlow while optimizing costs using preemptible VMs. Kubeflow Pipelines, on the other hand, provides a robust framework for distributed training and can be deployed on GKE for scalability. Other options, such as Vertex AI AutoML, local notebooks, and BigQuery ML, either lack the flexibility for custom TensorFlow training or do not meet the scalability and integration requirements.

  • A. Correct.

    Vertex AI custom training with preemptible VMs is a cost-effective and scalable option for training custom models. It allows you to use distributed training with TensorFlow and integrates well with Google Cloud services.

  • B. Correct.

    Kubeflow Pipelines with TensorFlow and GKE is a suitable choice for distributed training, as it offers scalability and supports TensorFlow workloads. It also integrates well with Google Cloud for deployment.

  • C. Incorrect.

    Vertex AI AutoML is designed for solving specific tasks like tabular data analysis but does not provide flexibility for custom training with TensorFlow.

  • D. Incorrect.

    Local Jupyter Notebooks do not offer scalability or seamless integration with Google Cloud services, making them unsuitable for production-level distributed training.

  • E. Incorrect.

    BigQuery ML is used for training models directly in BigQuery and is not designed for custom TensorFlow training workflows.

Timed practice exam

Take a Google Professional Machine Learning Engineer practice test under exam conditions

60 questions in 120 minutes, drawn from this bank, with a score report and a per-question review when you finish.

Start timed exam

More Google Professional Machine Learning Engineer practice questions

  1. 251.You are working on a machine learning project and need to train a custom model using TensorFlow....
  2. 252.Your team is building a machine learning model for predicting customer churn. You need to train the...
  3. 253.You are a Machine Learning Engineer tasked with training a custom NLP model on a large dataset. The...
  4. 254.You are a machine learning engineer tasked with deploying a tabular dataset classification model...
  5. 255.Your organization wants to build a machine learning workflow to predict sales using tabular data....
Back to the Google Professional Machine Learning Engineer exam dumps