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

Cloud Bigtable is a fully managed NoSQL database service designed to handle large analytical and operational workloads. One of its key features is the ability to replicate data across multiple geographic locations automatically, ensuring high availability and resilience. Here’s a detailed explanation:

Replication: Cloud Bigtable supports multi-cluster routing and replication across different geographic regions. This means that data can be replicated across multiple zones within a region or even across regions, providing geo-redundancy.

Automatic Handling: Once configured, Bigtable automatically manages replication without requiring manual intervention. This is in line with the company's policy for long-term data storage that necessitates automatic replication over at least two geographic places.

Use Case Suitability: Bigtable is ideal for applications that require low-latency access to large amounts of data, which makes it suitable for various use cases including analytical applications, IoT, and financial data processing.

Configuration: Setting up replication involves creating instances in multiple zones and configuring them to replicate data. Google Cloud’s management interface and APIs make this straightforward to configure and monitor.

References:

Google Cloud Bigtable Documentation

Google Cloud Storage Options

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 least-privilege access and provide necessary permissions to the DevOps team only during a deployment issue, follow these steps:

Create a Service Account:

In your Google Cloud project, create a new service account specifically for the DevOps team.

Assign Limited Permissions:

Grant the service account permissions with only the necessary list/view roles. For instance, you can create a custom IAM role with compute.instances.list and compute.instances.get permissions.

Grant Service Account User Role:

Assign the Service Account User role to the DevOps team members for the created service account. This allows them to act as the service account and use its permissions.

Access Control During Incidents:

During a deployment issue, the DevOps team can temporarily use the service account to access the resources. This ensures they have the least-privilege access required to investigate and resolve the issue.

Automation and Monitoring:

Implement automation to enable and disable the service account access as needed and monitor the usage to ensure compliance with the least-privilege principle.

Benefits:

Security: Limits access to only what is necessary, reducing the risk of unauthorized changes.

Flexibility: Provides necessary access during incidents without granting permanent elevated permissions.

References

Creating and Managing Service Accounts

Service Account User Role