Google Professional Data Engineer
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You have chosen Cloud Datastore to ingest vehicle telemetry data in real time. Your goal is to create a storage system that supports long-term data growth while minimizing costs. Additionally, you require the ability to periodically create snapshots of this data to enable point-in-time (PIT) recovery or to clone the data for use in a different Cloud Datastore environment. You aim to archive these snapshots for long-term storage. Which two methods can achieve these objectives? (Choose two.)
Explanation:
The correct answers are A and B. Option A involves using managed export to store the data in a Cloud Storage bucket using Nearline or Coldline class, which is a cost-effective solution for long-term data growth and archival. Option B involves using managed export and then importing the data to Cloud Datastore in a separate project under a unique namespace reserved for that export. This method helps in creating snapshots for point-in-time (PIT) recovery and cloning a copy of the data in a different environment, which fits the requirements of PIT recovery and long-term archival. Options C, D, and E are not as suitable due to cost implications, limitations with entity filters and inappropriate storage solutions, as highlighted in the highly voted answers and referenced documentation.