Google Professional Machine Learning Engineer
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Ultimate access to all questions.
You are a machine learning engineer tasked with building a TensorFlow text-to-image generative model. Your dataset contains billions of images along with their respective captions. You aim to design a low-maintenance, automated workflow, which must: (1) Read data from a Cloud Storage bucket, (2) Collect statistics, (3) Split the dataset into training, validation, and test datasets, (4) Perform necessary data transformations, (5) Train the model using the training and validation datasets, and (6) Validate the model using the test dataset. Given these requirements, which approach should you choose?
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.