Exam Professional Machine Learning Engineer topic 1 question 281 discussion

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

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.

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.

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

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.