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

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)

References:

Cloud Data Loss Prevention API Documentation

DLP API Client Libraries

To ensure that no personally identifiable information (PII) is published in your yearly website usage analytics reports while preserving data integrity, the Cloud Data Loss Prevention (Cloud DLP) API can be utilized to identify and transform PII within your datasets.​

Option A: Encrypting PII does not remove it from the reports; it merely obscures it, which may not be sufficient for compliance or privacy requirements.​

Option B: Discovering and transforming PII ensures that sensitive information is either masked, tokenized, or otherwise obfuscated, effectively removing PII from the reports while maintaining the overall structure and utility of the data.​

Option C: Detecting and deleting PII could lead to loss of valuable data and may disrupt the integrity of the reports.​

Option D: Quarantining PII data implies isolating it, which doesn't address the need to publish reports without PII.​

Therefore, Option B is the most appropriate approach, as it leverages the Cloud DLP API to identify and transform PII, ensuring that the published reports are free from sensitive information while preserving data integrity.​

References:

Cloud DLP Overview

De-identifying Sensitive Data

To securely access Google Cloud APIs from CI/CD pipelines running on Google Kubernetes Engine (GKE), follow these steps:

Create Service Accounts:

Create individual service accounts for each CI/CD pipeline. This ensures isolation and minimal permissions per pipeline.

Use a naming convention that includes an identifier for each pipeline, such as pipeline-a-sa, pipeline-b-sa, etc.

Configure Kubernetes Service Accounts:

Create Kubernetes service accounts for each CI/CD pipeline pod.

Map Kubernetes Service Accounts to Google Service Accounts:

Use Workload Identity to associate Kubernetes service accounts with the corresponding Google service accounts. This allows the pods to authenticate to Google Cloud APIs securely.

Example command to bind the Kubernetes service account to the Google service account:

gcloud iam service-accounts add-iam-policy-binding \ --role roles/iam.workloadIdentityUser \ --member "serviceAccount:.svc.id.goog[/]" \ @.iam.gserviceaccount.com

Deploy CI/CD Pipelines:

Ensure each pipeline runs in dedicated pods that use the specific Kubernetes service accounts configured earlier.

This setup ensures that each pipeline has the necessary permissions to interact with Google Cloud APIs securely, adhering to the principle of least privilege.

References

Using Workload Identity

Managing Service Accounts