Google Updated Professional-Cloud-Security-Engineer Exam Questions and Answers by nikolas

Enable VPC Service Controls:

VPC Service Controls help mitigate the risk of data exfiltration by allowing you to define a security perimeter around GCP resources.

Set up a service perimeter around your BigQuery project to restrict data access to within the defined perimeter.

Create Access Levels:

In the Google Cloud Console, navigate to the Access Context Manager.

Define access levels based on IP address conditions, specifying the authorized source IP addresses that are allowed to access your BigQuery resources.

These access levels are used to enforce policies that restrict who can access your sensitive data based on their IP addresses.

Apply Service Perimeter with Access Levels:

Apply the created access levels to the service perimeter to ensure that only requests originating from the specified IP addresses are able to access BigQuery tables.

This setup ensures that the sensitive PII data is not accessible from unauthorized IP addresses, reducing the risk of data exfiltration.

https://cloud.google.com/logging/docs/export/troubleshoot#errors_exporting_to_cloud_storage

https://cloud.google.com/logging/docs/export/troubleshoot

Unable to grant correct permissions to the destination: Even if the sink was successfully created with the correct service account permissions, this error message displays if the access control model for the Cloud Storage bucket was set to uniform access when the bucket was created. For existing Cloud Storage buckets, you can change the access control model for the first 90 days after bucket creation by using the Permissions tab. For new buckets, select the Fine-grained access control model during bucket creation. For details, see Creating Cloud Storage buckets.

To ensure user-uploaded comments and product reviews do not include sensitive data before publication, use the Cloud Data Loss Prevention (DLP) API.

Enable DLP API:

Go to the Cloud Console and navigate to APIs & Services > Library.

Search for "Data Loss Prevention API" and enable it.

Configure DLP API:

Create an inspection template specifying the types of sensitive data to detect.

Set up de-identification templates if you want to redact or mask sensitive data.

Implement DLP in Application:

Use the Google Cloud DLP Client Library for the desired programming language.

Send the text data to the DLP API for inspection before saving or publishing.

from google.cloud import dlp_v2 dlp_client = dlp_v2.DlpServiceClient() parent = f"projects/{project_id}" item = {"value": "User comment text here"} inspect_config = {"info_types": [{"name": "PERSON_NAME"}, {"name": "CREDIT_CARD_NUMBER"}]} response = dlp_client.inspect_content(parent=parent, inspect_config=inspect_config, item=item)

Cloud Data Loss Prevention API Documentation

DLP API Client Libraries

VPC Peering is between organizations not between Projects in an organization. That is Shared VPC. In this case, both projects are in same organization so having VPC Service Controls around both projects with necessary rules should be fine.

https://cloud.google.com/vpc-service-controls/docs/overview