Google Cloud Professional Data Engineer — Question 58

You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time. Which two methods can accomplish this?
(Choose two.)

Answer options

• A. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
• B. Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
• C. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
• D. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
• E. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.

Correct answer: A, B

Explanation

Option A is correct because using managed export to store data in a Cloud Storage bucket with Nearline or Coldline classes is cost-effective for long-term storage. Option B is also correct because importing the managed export into a separate Cloud Datastore project allows for a unique namespace, facilitating archiving. Options C, D, and E do not meet the requirements of long-term archiving and cost efficiency as effectively as A and B do.