Google Cloud Professional Machine Learning Engineer — Question 171

You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort. What should you do?

Answer options

• 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.

Correct answer: B

Explanation

The correct answer is B, as Vertex AI Feature Store is specifically designed for storing and managing features used in model training and online predictions. Option A is incorrect because while Bigtable is suitable for key/value data, it lacks the specific features for machine learning workflows. Option C is not ideal since a Vertex AI dataset does not provide the dedicated management capabilities of the Feature Store. Option D, while useful for analysis, is not the most efficient for managing and serving features for real-time predictions.