Google Professional Data Engineer Question 137
Single answerGoogle Cloud PlatformYour organization stores large datasets in Google Cloud Storage. These datasets are used for analytics workloads that occur only at the end of each quarter. To optimize costs, you want to implement a data lifecycle management strategy to move infrequently accessed data to a cheaper storage class without manual intervention. Which approach should you take?
- A
Manually move the data to the Nearline storage class at the end of each quarter.
- B
Set up a lifecycle management policy to transition objects to the Nearline storage class after 30 days of creation.
- C
Set up a lifecycle management policy to delete objects after 30 days.
- D
Set up a lifecycle management policy to transition objects to the Archive storage class immediately after upload.
Show answer and explanation
Correct answer: B
Explanation
Google Cloud Storage lifecycle management policies allow you to automate transitions between storage classes based on object age or other conditions. In this scenario, transitioning data to the Nearline storage class after 30 days provides a balance between cost savings and accessibility for quarterly analytics workloads. Nearline is optimized for infrequently accessed data, making it the ideal choice for this use case.
- A. Incorrect.
Manually managing the storage class transitions is error-prone and does not scale well. Additionally, it does not take advantage of automated lifecycle policies provided by Google Cloud Storage.
- B. Correct.
Setting up a lifecycle management policy to transition objects to a Nearline storage class after 30 days is a cost-effective solution that automates the process. This aligns with the data access pattern of being infrequent until the end of each quarter.
- C. Incorrect.
Deleting objects after 30 days would result in the loss of necessary data for quarterly analytics, making this option unsuitable.
- D. Incorrect.
Transitioning objects to the Archive storage class immediately after upload would make retrieval for quarterly analytics workloads expensive and slower, as Archive is designed for long-term storage with rare access.