Google Cloud Certified Professional Machine Learning Engineer Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose A1

  A) AI Platform

• 2

  The most suitable option for minimizing code refactoring and infrastructure overhead while enabling large-scale training on Google Cloud is: A. Use AI Platform for distributed training. * **Simplified Workflow:** AI Platform offers a managed service for training machine learning models. You can train your existing TensorFlow Estimator code with minimal changes, reducing the need for extensive code refactoring. * **Distributed Training:** AI Platform automatically handles distributing your training job across multiple machines, allowing you to leverage the power of Google's cloud infrastructure to train on large datasets efficiently. * **Reduced Infrastructure Overhead:** You don't need to manage the underlying infrastructure (e.g., setting up and maintaining a cluster) yourself. AI Platform takes care of all the infrastructure provisioning and management, minimizing the workload on your team.

• Chose A2

  I chose A. Even though D is a working option, it requires us to create a GKE cluster, which requires more work.