Google Professional Machine Learning Engineer Question 238
Single answerGoogle Cloud PlatformYou are designing a machine learning pipeline for a recommendation system, and you need to store a large dataset of user interactions that exceeds 10 TB in size. The dataset needs to be queried frequently for feature engineering and model training. Which Google Cloud service should you use to store the data while ensuring optimal performance and scalability for querying?
- A
Cloud Storage
- B
BigQuery
- C
Cloud SQL
- D
Cloud Spanner
Show answer and explanation
Correct answer: B
Explanation
BigQuery is the most appropriate choice for storing and querying large datasets efficiently in this scenario. It is designed to handle large-scale data and supports fast analytical queries, making it ideal for use cases such as feature engineering and model training in machine learning pipelines. Other options, such as Cloud Storage or Cloud SQL, are not optimized for querying large datasets at this scale.
- A. Incorrect.
Cloud Storage is suitable for storing unstructured data or large files, but it is not optimized for querying large datasets efficiently.
- B. Correct.
BigQuery is a fully managed, serverless data warehouse designed for scalable and fast SQL queries on large datasets, making it the ideal choice for this use case.
- C. Incorrect.
Cloud SQL is a managed relational database service, but it is not optimized for handling datasets at the scale of 10 TB or for frequent feature engineering queries.
- D. Incorrect.
Cloud Spanner is a globally distributed, horizontally scalable database, but it is more suitable for transactional use cases rather than analytical workloads like feature engineering and querying large datasets.