Google Professional Machine Learning Engineer exam dumps

Google Professional Machine Learning Engineer practice question 150 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 150

Select 2Google Cloud Platform

You are a Machine Learning Engineer tasked with setting up a collaborative environment for your data science team to develop and train machine learning models. The team requires integration with Google Cloud services, GPU/TPU support for training, and the ability to work within a fully managed environment. Which platform(s) would best meet these requirements?

  1. A

    Google Cloud Workbench

  2. B

    Colab Enterprise

  3. C

    Notebooks on Dataproc

  4. D

    JupyterLab installed on a local machine

  5. E

    Google Sheets

Show answer and explanation

Correct answers: A, B

Explanation

Google Cloud Workbench and Colab Enterprise both provide managed, collaborative environments that integrate seamlessly with Google Cloud services and support GPU/TPU acceleration. These features make them ideal for teams working on machine learning model development and training. Notebooks on Dataproc, while useful for Spark-based workflows, are not optimized for collaborative model development. Local JupyterLab installations and Google Sheets do not fulfill the requirements of being fully managed, cloud-integrated, and suitable for machine learning tasks.

  • A. Correct.

    Google Cloud Workbench is a fully managed environment designed for data science and machine learning workflows. It integrates well with Google Cloud services and supports GPU/TPU acceleration, making it a good choice for collaborative model development.

  • B. Correct.

    Colab Enterprise is a managed notebook environment specifically built for enterprise use. It supports GPU/TPU acceleration, integrates seamlessly with Google Cloud services, and is designed for collaboration, making it an ideal choice for the given requirements.

  • C. Incorrect.

    Notebooks on Dataproc are designed for use in Spark-based workflows and distributed data processing tasks. While they support integration with Google Cloud services, they are not as optimized for collaborative model development and training with GPUs/TPUs.

  • D. Incorrect.

    JupyterLab installed on a local machine can offer a development environment but lacks direct integration with Google Cloud services, managed infrastructure, and collaboration features, making it unsuitable for the requirements outlined.

  • E. Incorrect.

    Google Sheets is a spreadsheet application and is not designed for machine learning workflows or collaborative model development.

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