Google Cloud Professional Machine Learning Engineer — Question 212

You are building a TensorFlow text-to-image generative model by using a dataset that contains billions of images with their respective captions. You want to create a low maintenance, automated workflow that reads the data from a Cloud Storage bucket collects statistics, splits the dataset into training/validation/test datasets performs data transformations trains the model using the training/validation datasets, and validates the model by using the test dataset. What should you do?

Answer options

• A. Use the Apache Airflow SDK to create multiple operators that use Dataflow and Vertex AI services. Deploy the workflow on Cloud Composer.
• B. Use the MLFlow SDK and deploy it on a Google Kubernetes Engine cluster. Create multiple components that use Dataflow and Vertex AI services.
• C. Use the Kubeflow Pipelines (KFP) SDK to create multiple components that use Dataflow and Vertex AI services. Deploy the workflow on Vertex AI Pipelines.
• D. Use the TensorFlow Extended (TFX) SDK to create multiple components that use Dataflow and Vertex AI services. Deploy the workflow on Vertex AI Pipelines.

Correct answer: D

Explanation

The correct answer is D because TensorFlow Extended (TFX) is specifically designed for production-level machine learning workflows, making it suitable for building and deploying pipelines that include components for data ingestion, transformation, and model training. Options A, B, and C do not utilize TFX, which is optimized for TensorFlow projects, and thus may not provide the same level of integration and efficiency for this specific use case.