Google Sample Question 7 of 15

You are logged into the Vertex AI Pipeline UI and noticed that an automated production TensorFlow training pipeline finished three hours earlier than a typical run. You do not have access to production data for security reasons, but you have verified that no alert was logged in any of the ML system’s monitoring systems and that the pipeline code has not been updated recently. You want to assure the quality of the pipeline results as quickly as possible so you can determine whether to deploy the trained model. What should you do?

Source: Google Cloud OFFICIAL

Official sample question published by Google Cloud. WiseOwlLearns is not affiliated with Google LLC.

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A Use Vertex AI TensorBoard to check whether the training metrics converge to typical values. Verify pipeline input configuration and steps have the expected values. ✓ Correct
B Upgrade to the latest version of the Vertex SDK and re-run the pipeline.
C Determine the trained model’s location from the pipeline’s metadata in Vertex ML Metadata, and compare the trained model’s size to the previous model.
D Request access to production systems. Get the training data’s location from the pipeline’s metadata in Vertex ML Metadata, and compare data volumes of the current run to the previous run.
🦉 Explanation by WiseOwl Tutor™ — not endorsed by Google

TensorBoard provides a compact and complete overview of training metrics such as loss and accuracy over time. If the training converges with the model’s expected accuracy, the model can be deployed. Upgrading the SDK is unrelated to verifying this run. Model size is a good indicator of health but does not provide a complete overview. Requesting access to production data violates security practices and takes too long.

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