Google Professional Machine Learning Engineer exam dumps

Google Professional Machine Learning Engineer practice question 187 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 187

Select 2Google Cloud Platform

You are working on a machine learning project hosted on Google Cloud, and your team wants to track and compare the performance of various experiments to identify the best-performing model. Which approach should you take to efficiently track and run ML experiments while ensuring reproducibility?

  1. A

    Use Vertex AI Experiments to log and compare metrics, parameters, and artifacts across different runs.

  2. B

    Manually document all experiment parameters and results in a shared spreadsheet.

  3. C

    Leverage ML Metadata (MLMD) in Vertex AI Pipelines to track lineage and metadata of experiments.

  4. D

    Store all model training data and hyperparameters within a Google Cloud Storage bucket for auditing.

  5. E

    Use a version control system like Git to store experiment scripts alongside their results.

Show answer and explanation

Correct answers: A, C

Explanation

Effective tracking and running of ML experiments on Google Cloud requires tools designed for reproducibility, comparison, and lineage tracking. Vertex AI Experiments and ML Metadata (MLMD) in Vertex AI Pipelines provide specialized features for experiment tracking, ensuring that metrics, parameters, and dependencies are logged and easily accessible. These tools are purpose-built for machine learning workflows and integrate seamlessly with Google Cloud services.

  • A. Correct.

    Vertex AI Experiments offers a robust and scalable way to track metrics, parameters, and artifacts, making it a highly recommended tool for tracking and running ML experiments on Google Cloud.

  • B. Incorrect.

    Manually documenting experiment details in a spreadsheet is prone to human error, lacks scalability, and does not ensure reproducibility.

  • C. Correct.

    ML Metadata (MLMD) in Vertex AI Pipelines provides detailed tracking of experiment lineage, which is essential for reproducibility and understanding dependencies between artifacts.

  • D. Incorrect.

    Storing training data and hyperparameters in Google Cloud Storage is useful for storage purposes, but it does not provide a system for tracking experiments or comparing results.

  • E. Incorrect.

    While Git is useful for versioning code, it is not designed to track experiment metrics, artifacts, or lineage.

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