Google Cloud Professional Machine Learning Engineer — Question 275

You work at a large organization that recently decided to move their ML and data workloads to Google Cloud. The data engineering team has exported the structured data to a Cloud Storage bucket in Avro format. You need to propose a workflow that performs analytics, creates features, and hosts the features that your ML models use for online prediction. How should you configure the pipeline?

Answer options

• 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.

Correct answer: B

Explanation

Option B is correct because BigQuery is well-suited for analytics and can efficiently handle large datasets. Using a Dataflow pipeline to create features and storing them in Vertex AI Feature Store allows for seamless integration with ML models for online predictions. The other options either use Cloud Spanner, which is not optimized for analytics in this context, or suggest storing features in ways that are not as conducive to online predictions.