Get Professional-Data-Engineer Dumps : Verified Google Professional Data Engineer Exam

Identity and Access Management (IAM) is the recommended way to control access to Pub/Sub resources, such as topics and subscriptions. IAM allows you to grant roles and permissions to users and service accounts at the project level or the individual resource level. You can also use IAM conditions to specify additional attributes for granting or denying access, such as time, date, or origin. By using IAM conditions, you can ensure that only the resources in project A can access the data in the project A topic, regardless of the network configuration or the VPC Service Controls. You can also prevent project B and any future project from accessing the data in the project A topic by not granting them any roles or permissions on the topic.

Option A is not a good solution, as VPC Service Controls are designed to prevent data exfiltration from Google Cloud resources to the public internet, not to control access between Google Cloud projects. VPC Service Controls create a perimeter around the resources of one or more projects, and restrict the communication with resources outside the perimeter. However, VPC Service Controls do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, configuring VPC Service Controls with a perimeter around the VPC of project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions.

Option B is not a good solution, as firewall rules are used to control the ingress and egress traffic to and from the VPC network of a project. Firewall rules do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, adding firewall rules in project A to only permit traffic from the VPC in project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions.

Option C is not a good solution, as VPC Service Controls are designed to prevent data exfiltration from Google Cloud resources to the public internet, not to control access between Google Cloud projects. VPC Service Controls create a perimeter around the resources of one or more projects, and restrict the communication with resources outside the perimeter. However, VPC Service Controls do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, configuring VPC Service Controls with a perimeter around project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions. References: Access control with IAM | Cloud Pub/Sub Documentation | Google Cloud, [Using IAM Conditions | Cloud IAM Documentation | Google Cloud], [VPC Service Controls overview | Google Cloud], [Using VPC Service Controls | Google Cloud], [Pub/Sub tier capabilities | Memorystore for Redis | Google Cloud].

This option will allow you to store the data in a low-cost storage option, as the archive storage class has the lowest price per GB among the Cloud Storage classes. It will also ensure that the data is immutable for 3 years, as the locked retention policy prevents the deletion or overwriting of the data until the retention period expires. You can still query the data using SQL by creating a BigQuery external table that references the exported files in the Cloud Storage bucket. Option A is incorrect because creating a BigQuery table clone will not reduce the storage costs, as the clone will have the same size and storage class as the original table. Option B is incorrect because creating a BigQuery table snapshot will also not reduce the storage costs, as the snapshot will have the same size and storage class as the original table. Option C is incorrect because enabling versioning on the bucket will not make the data immutable, as the versions can still be deleted or overwritten by anyone with the appropriate permissions. It will also increase the storage costs, as each version of the file will be charged separately. References:

• Exporting table data | BigQuery | Google Cloud
• Storage classes | Cloud Storage | Google Cloud
• Retention policies and retention periods | Cloud Storage | Google Cloud
• Federated queries | BigQuery | Google Cloud