Google Professional Machine Learning Engineer Question 441
Select 3Google Cloud PlatformYou are working on a machine learning workflow in Google Cloud and need to track metadata such as dataset versions, model parameters, and evaluation metrics across different experiments. You decide to use Vertex ML Metadata to achieve this. Which of the following actions can Vertex ML Metadata help you perform?
- A
Track the lineage of datasets, models, and experiments.
- B
Store and manage raw training data for large-scale datasets.
- C
Automatically visualize the relationship between artifacts and executions.
- D
Query and compare metadata across multiple experiments.
- E
Host trained models for online prediction.
Show answer and explanation
Correct answers: A, C, D
Explanation
Vertex ML Metadata is a powerful tool for tracking and analyzing metadata in your machine learning workflows. It helps you maintain experiment lineage, visualize relationships between components, and query metadata to improve your pipeline. However, it does not handle raw data storage or model hosting, as those are outside its scope.
- A. Correct.
Vertex ML Metadata is designed to track the lineage of datasets, models, and experiments, allowing users to understand how artifacts and executions are related.
- B. Incorrect.
Vertex ML Metadata does not store or manage raw training data. It is specifically designed for metadata tracking and lineage, not for data storage.
- C. Correct.
Vertex ML Metadata provides automatic visualization of artifact and execution relationships, helping you understand the flow of your workflow.
- D. Correct.
Vertex ML Metadata allows querying and comparing metadata across experiments, which is essential for analyzing and improving workflows.
- E. Incorrect.
Hosting trained models for online prediction is not a function of Vertex ML Metadata. This is handled by services like Vertex AI Model Deployment.