Google Cloud Professional Machine Learning Engineer — Question 22

You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

Answer options

• A. Use AI Platform for distributed training.
• B. Create a cluster on Dataproc for training.
• C. Create a Managed Instance Group with autoscaling.
• D. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.

Correct answer: A

Explanation

The correct answer is A because AI Platform is specifically designed to handle distributed training efficiently, which aligns with the need for high performance on large datasets. The other options, while useful for different scenarios, would require more significant changes or do not provide the same level of integration with TensorFlow Estimators as AI Platform.