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Answer: Load the data in BigQuery. Use BigQuery ML to train a matrix factorization model.
The correct answer is B. Using BigQuery ML to train a matrix factorization model requires the least amount of coding compared to other options. BigQuery ML provides high-level APIs for machine learning tasks directly within the BigQuery environment, reducing the need for extensive coding and data movement. Matrix factorization is a commonly used collaborative filtering technique for recommendation systems, making it suitable for recommending new games to users based on gameplay time data, user metadata, and game metadata.
Author: LeetQuiz Editorial Team
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You work at a gaming startup that has accumulated several terabytes of structured data stored in Cloud Storage. This data encompasses gameplay time data, user metadata, and game metadata. Your objective is to create a recommendation model to suggest new games to users. Given your primary goal is to minimize the amount of coding required, what approach should you take?
A
Load the data in BigQuery. Use BigQuery ML to train an Autoencoder model.
B
Load the data in BigQuery. Use BigQuery ML to train a matrix factorization model.
C
Read data to a Vertex AI Workbench notebook. Use TensorFlow to train a two-tower model.
D
Read data to a Vertex AI Workbench notebook. Use TensorFlow to train a matrix factorization model.
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