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?
For a workflow that includes performing analytics, creating features, and hosting features for online prediction in Google Cloud, the best approach would be to ingest the Avro files into BigQuery for analytics. BigQuery is a powerful analytics data warehouse that can easily handle structured data and perform complex queries efficiently. Then, using a Dataflow pipeline to create the necessary features ensures scalable and flexible data processing. Finally, storing the created features in Vertex AI Feature Store is ideal for managing and serving these features with low latency required for online prediction. This combination leverages the strengths of each tool in Google Cloud's ecosystem effectively.
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
Vertex AI Feature Store is designed for managing and serving features for online prediction with low latency.
I think the answer is A because BigQuery does not support Avro format but CloudSpanner does.
FYI BigQuery supports the Avro format. Please check your facts
"performs analytics" = Bigquery "hosts the features" = Vertex AI Feature Store"
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.