Google Professional Machine Learning Engineer
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Ultimate access to all questions.
As a machine learning engineer at a large organization, you are tasked with migrating the company's ML and data workloads to Google Cloud. The data engineering team has provided you with structured data exported to a Cloud Storage bucket in Avro format. Your project requires setting up a workflow that will perform data analytics, create features for the ML model, and host these features for online predictions. How should you configure the pipeline?
A
Ingest the Avro files into Cloud Spanner to perform analytics. Use a Dataflow pipeline to create the features, and store them in Vertex AI Feature Store for online prediction.
B
Ingest the Avro files into BigQuery to perform analytics. Use a Dataflow pipeline to create the features, and store them in Vertex AI Feature Store for online prediction.
C
Ingest the Avro files into Cloud Spanner to perform analytics. Use a Dataflow pipeline to create the features, and store them in BigQuery for online prediction.
D
Ingest the Avro files into BigQuery to perform analytics. Use BigQuery SQL to create features and store them in a separate BigQuery table for online prediction.