How to Configure an ML Feature Pipeline on GCP?

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?

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.
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. Source Reference Answer
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.
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.

Community Votes

100%

100% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

This question tests the ability to select the appropriate Google Cloud services for specific data processing tasks, distinguishing between analytical databases (BigQuery) and transactional databases (Spanner), and identifying the correct solution for low-latency online serving.

The optimal workflow involves using BigQuery for analytics, Dataflow for feature engineering, and Vertex AI Feature Store for low-latency online serving. The community consensus confirms that this combination effectively addresses the requirements for analysis, transformation, and hosting.

A common error is selecting Cloud Spanner for analytics, as it is an OLTP database designed for transactional consistency rather than large-scale analytical processing.

Community Discussion (6 comments)

guilhermebutzke 👍 9 Selected: B

My Answer: B “You need to propose a workflow that performs analytics, creates features, and hosts ”: Ingest the Avro files into BigQuery to perform analytics “workflow that performs analytics, creates features”: Dataflow pipeline to create the features “and hosts the features that your ML models use for online prediction”:store them in Vertex AI Feature Store for online prediction

carolctech 👍 1 Selected: B

B) BigQuery is designed for large-scale analytics, while Spanner (options A and C) is not, since it is more suited for transactional workloads. The Dataflow pipeline should be used to transform the Avro files into Parquet before ingesting it into BigQuery and is also optimal for feature engineering tasks. Vertex AI Feature Store is specifically designed for online feature management and serving, while storing features in BigQuery is not the best option for online prediction, due to potential latency.

AzureDP900 👍 1

B is right The original audio recordings have an 8 kHz sample rate, which is sufficient for speech recognition. Using the Speech-to-Text API with synchronous recognition would require your application to wait for the transcription process to complete before proceeding. This could lead to performance issues and delays in processing large volumes of audio data. Asynchronous recognition, on the other hand, allows your application to continue processing without waiting for the transcription process to complete. The transcribed text can be retrieved later when needed.

VinaoSilva 👍 1 Selected: B

"performs analytics" = Bigquery "hosts the features" = Vertex AI Feature Store"

emsherff 👍 2 Selected: B

Vertex AI Feature Store is designed for managing and serving features for online prediction with low latency.

MultiCloudIronMan 👍 1 Selected: A

I think the answer is A because BigQuery does not support Avro format but CloudSpanner does.

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Expert Analysis

Why the Answer Is Correct

BigQuery is the correct choice for performing analytics on structured data like Avro files because it is a serverless, highly scalable enterprise data warehouse. Dataflow is the ideal service for creating features, as it provides a serverless execution environment for robust data processing pipelines using Apache Beam. Finally, Vertex AI Feature Store is specifically designed to host and serve features for online prediction with low latency, which standard databases cannot efficiently provide.

Why the Other Options Are Wrong

Options A and C incorrectly suggest using Cloud Spanner for analytics; Spanner is optimized for horizontal scaling and strong consistency in transactional workloads, not for running analytical queries. Option D incorrectly proposes storing features in BigQuery for online prediction; while BigQuery is excellent for batch analytics and training, it is not designed for the low-latency, high-throughput serving required for real-time online predictions.

Community Comment Notes

Community members strongly support Option B, emphasizing that BigQuery is designed for large-scale analytics while Spanner is suited for transactional workloads. Commenters also highlighted that Vertex AI Feature Store is the dedicated service for managing and serving features for online prediction, ensuring the necessary performance for ML models.

Official Reference

Exam Strategy

When designing ML workflows, map the specific technical requirement to the service's core strength: BigQuery for analytics, Dataflow for complex ETL/feature engineering, and Vertex AI Feature Store for low-latency serving. Avoid using transactional databases like Spanner for analytical reporting.

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