Question 238 of 280
Single answerYou 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?
✓ASet up Vertex AI Experiments to track metrics and parameters. Configure Vertex AI TensorBoard for visualization.
BSet 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.
CSet 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.
DSet 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.
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Correct Answer: A
Set up Vertex AI Experiments to track metrics and parameters. Configure Vertex AI TensorBoard for visualization.
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Explanation
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