Google Professional Machine Learning Engineer exam dumps

Google Professional Machine Learning Engineer practice question 197 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 197

Select 4Google Cloud Platform

You are tasked with building and deploying a machine learning model in Google Cloud, and your team emphasizes the importance of tracking experiments to optimize the model's performance. You decide to use Vertex AI Experiments. How can you effectively track, compare, and analyze the experiments to identify the best-performing model?

  1. A

    Use Vertex AI Experiments to log hyperparameters, metrics, and artifacts for each experiment run.

  2. B

    Leverage Vertex AI TensorBoard integration to visualize and compare experiment metrics such as accuracy and loss over time.

  3. C

    Manually record experiment results in a spreadsheet for better control over the data.

  4. D

    Utilize Vertex AI Pipelines to automate the workflow and ensure experiment runs are reproducible.

  5. E

    Export experiment data from Vertex AI Experiments to BigQuery for advanced analysis and reporting.

Show answer and explanation

Correct answers: A, B, D, E

Explanation

Vertex AI provides a robust ecosystem for managing machine learning experiments. Using Vertex AI Experiments, you can log and track metrics, hyperparameters, and artifacts. TensorBoard integration helps with visualizing performance over time, while Vertex AI Pipelines ensures automated and reproducible workflows. Additionally, exporting data to BigQuery allows for advanced analysis. Manually tracking experiments in spreadsheets is not recommended due to scalability and error concerns.

  • A. Correct.

    Correct. Vertex AI Experiments allows you to log hyperparameters, metrics, and artifacts, which is essential for tracking the details of each experimental run.

  • B. Correct.

    Correct. Integrating with Vertex AI TensorBoard helps you visualize and compare metrics across different experiments, making it easier to analyze performance trends.

  • C. Incorrect.

    Incorrect. While spreadsheets can be helpful for small-scale tracking, they are error-prone, manual, and not scalable compared to using Vertex AI's built-in experiment management tools.

  • D. Correct.

    Correct. Vertex AI Pipelines ensures that your workflows are automated and reproducible, which is crucial for consistent experimentation and analysis.

  • E. Correct.

    Correct. Exporting data to BigQuery enables advanced querying, analysis, and reporting, which can provide deeper insights into your experiments.

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