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Answer: Store features in Vertex AI Feature Store.
The correct answer is B, which is 'Store features in Vertex AI Feature Store.' Vertex AI Feature Store is specifically designed for storing and serving machine learning features with low latency, which is essential for online prediction. It also supports point-in-time retrieval of historical data, making it easier for the data science team to train models accurately. Additionally, Vertex AI Feature Store is optimized for minimal effort in terms of setup and maintenance, fulfilling all the requirements mentioned in the question.
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You work for a leading delivery company that handles thousands of deliveries per day. You need to design a system that efficiently stores and manages real-time and historical data features such as parcels delivered and truck locations over time. This system must be capable of retrieving these features with low latency to feed them into a model for online prediction. Additionally, the data science team should be able to easily retrieve historical data at specific points in time for model training purposes. Given these requirements, you also want to ensure that setting up and maintaining this system requires minimal effort on your part. What should you do?
A
Store features in Bigtable as key/value data.
B
Store features in Vertex AI Feature Store.
C
Store features as a Vertex AI dataset, and use those features to train the models hosted in Vertex AI endpoints.
D
Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.
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