Google Cloud Professional Machine Learning Engineer — Question 237

You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework. Your team runs multiple ML experiments each week, which makes it difficult to track the experiment runs. You want a simple approach to effectively track, visualize, and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?

Answer options

• A. Set up Vertex AI Experiments to track metrics and parameters. Configure Vertex AI TensorBoard for visualization.
• B. Set up a Cloud Function to write and save metrics files to a Cloud Storage bucket. Configure a Google Cloud VM to host TensorBoard locally for visualization.
• C. Set up a Vertex AI Workbench notebook instance. Use the instance to save metrics data in a Cloud Storage bucket and to host TensorBoard locally for visualization.
• D. Set up a Cloud Function to write and save metrics files to a BigQuery table. Configure a Google Cloud VM to host TensorBoard locally for visualization.

Correct answer: A

Explanation

The correct answer is A because Vertex AI Experiments is specifically designed for tracking metrics and parameters of ML experiments efficiently, while Vertex AI TensorBoard provides integrated visualization capabilities. The other options involve more complex setups and local hosting of TensorBoard, which increases overhead and complexity compared to using Vertex AI's built-in tools.