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Google Cloud Certified Professional Cloud Developer · Google Cloud
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The two actions you should take are: B. Create a dataset in the EU region that will keep information about EU users only. This directly addresses the requirement to store EU user data in a single EU region. E. Use DML statements in BigQuery to update/delete user records based on their requests. This allows you to efficiently delete user account information upon request, fulfilling the GDPR requirement.
Here's why the other options are less suitable: A. Use BigQuery federated queries to query data from Cloud Storage: Federated queries are useful for accessing data in other sources, but they don't solve the problem of storing EU user data in a specific region or providing a mechanism for deleting user data. C. Create a Cloud Storage bucket in the EU region to store information for EU users only: While Cloud Storage can be used for data storage, it's not the primary data warehouse for your application. You'd need to manage data movement between BigQuery and Cloud Storage, which adds complexity. D. Re-upload your data using to a Cloud Dataflow pipeline by filtering your user records out: This is a time-consuming and potentially disruptive process. It's not the most efficient way to handle user data deletion requests.
B. Creating a separate dataset in the EU region for EU users allows your company to ensure that all data for these users is stored in a specific geographic location, complying with regional data residency requirements. BigQuery allows you to select the region where your dataset resides, and having a dedicated dataset for EU users makes it easier to manage and enforce policies specific to EU data. E. Using Data Manipulation Language (DML) statements in BigQuery (such as DELETE and UPDATE) enables your company to comply with requests to delete or modify user account information. This capability is essential for adhering to regulations like the GDPR, which may require companies to delete users' personal data upon request.