Topic 1 Question 276
You work at a gaming startup that has several terabytes of structured data in Cloud Storage. This data includes gameplay time data, user metadata, and game metadata. You want to build a model that recommends new games to users that requires the least amount of coding. What should you do?
Load the data in BigQuery. Use BigQuery ML to train an Autoencoder model.
Load the data in BigQuery. Use BigQuery ML to train a matrix factorization model.
Read data to a Vertex AI Workbench notebook. Use TensorFlow to train a two-tower model.
Read data to a Vertex AI Workbench notebook. Use TensorFlow to train a matrix factorization model.
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- 正解だと思う選択肢: B
least amount of coding--> BQML recommendations--> matrix factorization
👍 4vaibavi2024/08/20 - 正解だと思う選択肢: B👍 2Yan_X2024/08/11
- 正解だと思う選択肢: B
Using BigQuery ML for training a matrix factorization model would require less coding compared to building a custom model with TensorFlow in a Vertex AI Workbench notebook. BigQuery ML provides high-level APIs for machine learning tasks directly within the BigQuery environment, thus reducing the amount of coding needed for data preprocessing and model training. Matrix factorization is a commonly used technique for recommendation systems, making it a suitable choice for recommending new games to users based on their gameplay time data, user metadata, and game metadata.
👍 2guilhermebutzke2024/08/19
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