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Google Professional-Cloud-Security-Engineer Exam With Confidence Using Practice Dumps

Exam Code:
Professional-Cloud-Security-Engineer
Exam Name:
Google Cloud Certified - Professional Cloud Security Engineer
Certification:
Vendor:
Questions:
318
Last Updated:
May 13, 2026
Exam Status:
Stable
Google Professional-Cloud-Security-Engineer

Professional-Cloud-Security-Engineer: Google Cloud Certified Exam 2025 Study Guide Pdf and Test Engine

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Google Cloud Certified - Professional Cloud Security Engineer Questions and Answers

Question 1

Your company’s cloud security policy dictates that VM instances should not have an external IP address. You need to identify the Google Cloud service that will allow VM instances without external IP addresses to connect to the internet to update the VMs. Which service should you use?

Options:

A.

Identity Aware-Proxy

B.

Cloud NAT

C.

TCP/UDP Load Balancing

D.

Cloud DNS

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Question 2

Your organization uses BigQuery to process highly sensitive, structured datasets. Following the "need to know" principle, you need to create the Identity and Access Management (IAM) design to meet the needs of these users:

• Business user must access curated reports.

• Data engineer: must administrate the data lifecycle in the platform.

• Security operator: must review user activity on the data platform.

What should you do?

Options:

A.

Configure data access log for BigQuery services, and grant Project Viewer role to security operators.

B.

Generate a CSV data file based on the business user's needs, and send the data to their email addresses.

C.

Create curated tables in a separate dataset and assign the role roles/bigquery.dataViewer.

D.

Set row-based access control based on the "region" column, and filter the record from the United States for data engineers.

Question 3

Your organization is developing a sophisticated machine learning (ML) model to predict customer behavior for targeted marketing campaigns. The BigQuery dataset used for training includes sensitive personal information. You must design the security controls around the AI/ML pipeline. Data privacy must be maintained throughout the model's lifecycle and you must ensure that personal data is not used in the training process Additionally, you must restrict access to the dataset to an authorized subset of people only. What should you do?

Options:

A.

Implement at-rest encryption by using customer-managed encryption keys (CMEK) for the pipeline. Implement strict Identity and Access Management (IAM) policies to control access to BigQuery.

B.

De-identify sensitive data before model training by using Cloud Data Loss Prevention (DLP) APIs, and implement strict Identity and Access Management (IAM) policies to control access to BigQuery.

C.

Implement Identity-Aware Proxy to enforce context-aware access to BigQuery and models based on user identity and device.

D.

Deploy the model on Confidential VMs for enhanced protection of data and code while in use. Implement strict Identity and Access Management (IAM) policies to control access to BigQuery.