Exam Professional Machine Learning Engineer topic 1 question 243 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 243
Topic #: 1

[All Professional Machine Learning Engineer Questions]

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?

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.

Show Suggested Answer

Suggested Answer: A 🗳️

by pikachu007 at Jan. 13, 2024, 9:07 a.m.

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b1a8fae

Highly Voted 1 year ago

Selected Answer: A

You want to run, track, visualize ML experiments -> look no further, Vertex AI experiments.

upvoted 7 times

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nnn245bbb

Most Recent 3 months, 1 week ago

Selected Answer: A

Based on ChatGPT

upvoted 1 times

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fitri001

10 months ago

Selected Answer: A

Built-in Tracking: Vertex AI Experiments is specifically designed for tracking ML experiments on Google Cloud. It simplifies logging metrics and parameters, eliminating the need for custom code. TensorBoard Integration: Vertex AI integrates with TensorBoard, allowing visualization of training logs and metrics directly within the Experiments interface. This provides a centralized location for both tracking and visualization. Minimized Overhead: This approach leverages existing services, minimizing the need for additional code or infrastructure setup compared to options with Cloud Functions or VMs.

upvoted 2 times

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pikachu007

1 year, 1 month ago

Selected Answer: A

Options B and D: These options involve more setup and maintenance overhead, as they require managing Cloud Functions, VMs, and storage resources. Option C: Vertex AI Workbench is excellent for interactive experimentation, but it's not optimized for long-term experiment tracking and visualization.

upvoted 3 times

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