Google Cloud Professional Machine Learning Engineer — Question 172

You have created a Vertex AI pipeline that automates custom model training. You want to add a pipeline component that enables your team to most easily collaborate when running different executions and comparing metrics both visually and programmatically. What should you do?

Answer options

• A. Add a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Query the table to compare different executions of the pipeline. Connect BigQuery to Looker Studio to visualize metrics.
• B. Add a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Load the table into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.
• C. Add a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Use Vertex AI Experiments to compare different executions of the pipeline. Use Vertex AI TensorBoard to visualize metrics.
• D. Add a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Load the Vertex ML Metadata into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.

Correct answer: C

Explanation

The correct answer is C because using Vertex ML Metadata along with Vertex AI Experiments and TensorBoard provides a comprehensive framework for collaboration and visualization of metrics during different pipeline executions. Options A and B rely on BigQuery, which is less integrated for real-time experimentation and visualization in this context. Option D, while it involves Vertex ML Metadata, uses pandas and Matplotlib, which are not as effective for collaborative visualizations as TensorBoard.