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Exam Professional Machine Learning Engineer All Questions

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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.
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Suggested Answer: A 🗳️
by pikachu007 at Jan. 13, 2024, 9:07 a.m.

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b1a8fae
Highly Voted 9 months, 1 week 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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fitri001
Most Recent 6 months, 1 week 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
9 months, 2 weeks 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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