Google Professional-Cloud-Security-Engineer Questions - Latest Professional-Cloud-Security-Engineer Dumps [2026]

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The Google Cloud Certified - Professional Cloud Security Engineer Exam certification exam tests the candidate's knowledge of various aspects of cloud security, such as access control, data protection, identity management, compliance, and audit logging. Professional-Cloud-Security-Engineer Exam also evaluates the candidate's ability to implement security solutions using GCP tools and services, and to design and implement security policies for GCP solutions.

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To prepare for the exam, candidates are encouraged to take advantage of the various resources provided by Google Cloud. These resources include online training courses, practice exams, and study guides. In addition, candidates are encouraged to gain practical experience by working on real-world cloud security projects.

Google Cloud Certified - Professional Cloud Security Engineer Exam Sample Questions (Q196-Q201):

NEW QUESTION # 196
When working with agents in a support center via online chat, an organization's customers often share pictures of their documents with personally identifiable information (PII). The organization that owns the support center is concerned that the PII is being stored in their databases as part of the regular chat logs they retain for review by internal or external analysts for customer service trend analysis.
Which Google Cloud solution should the organization use to help resolve this concern for the customer while still maintaining data utility?

A. Use Object Lifecycle Management to make sure that all chat records with PII in them are discarded and not saved for analysis.
B. Use Cloud Key Management Service (KMS) to encrypt the PII data shared by customers before storing it for analysis.
C. Use the image inspection and redaction actions of the DLP API to redact PII from the images before storing them for analysis.
D. Use the generalization and bucketing actions of the DLP API solution to redact PII from the texts before storing them for analysis.

Answer: D

Explanation:
Explanation/Reference:
Reference; https://cloud.google.com/dlp/docs/deidentify-sensitive-data

NEW QUESTION # 197
Your organization has 3 TB of information in BigQuery and Cloud SQL. You need to develop a cost- effective, scalable, and secure strategy to anonymize the personally identifiable information (PII) that exists today. What should you do?

A. Create a new BigQuery dataset and Cloud SQL instance. Copy a small subset of the data to these new locations. Use Cloud Data Loss Prevention API to scan this subset for PII. Based on the results, create a custom anonymization script and apply the script to the entire 3 TB dataset in the original locations.
B. Export all 3TB of data from BigQuery and Cloud SQL to Cloud Storage. Use Cloud Sensitive Data Protection to anonymize the exported data. Re-import the anonymized data back into BigQuery and Cloud SQL.
C. Scan your BigQuery and Cloud SQL data using the Cloud DLP data profiling feature. Use the data profiling results to create a de-identification strategy with either Cloud Sensitive Data Protection's de- identification templates or custom configurations.
D. Inspect a representative sample of the data in BigQuery and Cloud SQL to identify PII. Based on this analysis, develop a custom script to anonymize the identified PII.

Answer: C

Explanation:
The problem requires a cost-effective, scalable, and secure strategy to anonymize 3 TB of PII in BigQuery and Cloud SQL.
Cloud Sensitive Data Protection (Cloud DLP): Cloud DLP is Google Cloud's fully managed service designed for discovering, classifying, and de-identifying sensitive data, including PII, across various Google Cloud services and on-premises environments. It is specifically built for scale and security.
Extract Reference: "Sensitive Data Protection helps you discover, classify, and de-identify sensitive data." and
"Sensitive Data Protection includes advanced de-identification techniques, such as tokenization, masking, and format-preserving encryption, to help you protect your sensitive data while preserving its utility for analysis." (Google Cloud Documentation: "Sensitive Data Protection overview" - https://cloud.google.com/sensitive- data-protection/docs/overview) Data Profiling: Cloud DLP's data profiling feature automatically scans your data (e.g., BigQuery tables, Cloud SQL databases) to identify where sensitive data resides and to understand its characteristics. This is crucial for developing an effective de-identification strategy.
Extract Reference: "Data profiles provide metrics and insights about your sensitive and high-risk data, helping you make informed decisions about data protection, access, and storage." and "When you configure Sensitive Data Protection to profile data in BigQuery, Cloud Storage, and Datastore, it automatically scans your data for sensitive information." (Google Cloud Documentation: "About data profiles" - https://cloud.google.com
/sensitive-data-protection/docs/concepts-data-profiles)
De-identification Templates and Custom Configurations: After profiling identifies the PII, Cloud DLP offers various de-identification methods, which can be applied using pre-built templates or custom configurations.
This allows for flexible and targeted anonymization. For BigQuery specifically, DLP can integrate with remote functions for de-identification at query time, minimizing data movement.
Extract Reference: "De-identification techniques, like encryption, obfuscate raw sensitive identifiers in your data. These techniques let you preserve the utility of your data for joining or analytics, while reducing the risk of handling the data." and "You can use this tutorial to replace that pipeline with a SQL query for only re- identification or both de-identification and re-identification." (Google Cloud Documentation: "De-identify BigQuery data at query time | Sensitive Data Protection Documentation" - https://cloud.google.com/sensitive- data-protection/docs/deidentify-bq-tutorial) Let's evaluate the other options:
B). Copy small subset... create a custom anonymization script... apply the script to the entire 3 TB dataset:
Relying on a "custom anonymization script" for 3 TB of data is generally not scalable, cost-effective, or secure compared to a managed service like Cloud DLP. Custom scripts require significant development, testing, maintenance, and robust error handling for large datasets, and might introduce security vulnerabilities.
C). Export all 3TB of data... to Cloud Storage... anonymize... Re-import: While feasible, exporting and re- importing 3 TB of data is a very time-consuming and potentially costly process due to data transfer and storage operations. Cloud DLP can often process data in-place or integrate more efficiently, especially with BigQuery. This option might not be the most cost-effective or efficient.
D). Inspect a representative sample... develop a custom script: Similar to option B, this relies on a custom script, which is less ideal for scalability, cost-effectiveness, and security than a managed service like Cloud DLP for 3 TB of sensitive data. Sample inspection is a good initial step, but the subsequent custom script for anonymization is the weak point.
Therefore, using Cloud DLP's data profiling to inform a de-identification strategy with its managed templates and configurations is the most appropriate, scalable, secure, and cost-effective approach for anonymizing PII in BigQuery and Cloud SQL.

NEW QUESTION # 198
Your organization operates Virtual Machines (VMs) with only private IPs in the Virtual Private Cloud (VPC) with internet access through Cloud NAT. Everyday, you must patch all VMs with critical OS updates and provide summary reports.
What should you do?

A. Ensure that VM Manager is installed and running on the VMs. In the OS patch management service, configure the patch jobs to update with critical patches dally.
B. Validate that the egress firewall rules allow any outgoing traffic. Log in to each VM and execute OS specific update commands. Configure the Cloud Scheduler job to update with critical patches daily for daily updates.
C. Copy the latest patches to the Cloud Storage bucket. Log in to each VM, download the patches from the bucket, and install them.
D. Assign public IPs to VMs. Validate that the egress firewall rules allow any outgoing traffic. Log in to each VM, and configure a daily cron job to enable for OS updates at night during low activity periods.

Answer: A

Explanation:
VM manager is a suite of tools used to automate managing of the fleet of VMs (including OS patching).
https://cloud.google.com/compute/docs/vm-manager

NEW QUESTION # 199
A customer has 300 engineers. The company wants to grant different levels of access and efficiently manage IAM permissions between users in the development and production environment projects.
Which two steps should the company take to meet these requirements? (Choose two.)

A. Create an Organizational Policy constraint for each folder environment.
B. Create a folder for each development and production environment.
C. Create a project with multiple VPC networks for each environment.
D. Create a Google Group for the Engineering team, and assign permissions at the folder level.
E. Create projects for each environment, and grant IAM rights to each engineering user.

Answer: B,D

Explanation:
To manage IAM permissions efficiently for a large engineering team with different levels of access in development and production environments, follow these steps:
* Create Separate Folders:
* Create a folder for the development environment.
* Create a folder for the production environment.
* This allows you to organize projects and apply different policies and permissions to each environment.
* Navigate to IAM & Admin in the GCP Console.
* Select "Folders" from the left-hand menu.
* Create a new folder named "Development".
* Create a new folder named "Production".
* Create Google Groups:
* Create Google Groups for different teams within the engineering department (e.g., Development Team, Production Team).
* This helps in managing permissions centrally.
* Use the Google Admin Console to create groups.
* Add relevant engineers to each group.
* Assign Permissions at the Folder Level:
* Assign appropriate IAM roles to the Google Groups at the folder level.
* For example, grant Viewer role to the Development Team group for the development folder.
* Grant Editor or more restrictive roles as required for the Production Team group for the production folder.
* Select the development folder.
* Go to the "Permissions" tab.
* Click on "Add" and enter the email address of the Development Team Google Group.
* Assign the "Viewer" role.
* Repeat for the production folder, assigning appropriate roles to the Production Team Google Group.
By following these steps, you create a clear separation between development and production environments and manage permissions efficiently using Google Groups and folders.
References:
* Google Cloud IAM Documentation
* Google Cloud Resource Manager Documentation

NEW QUESTION # 200
You have stored company approved compute images in a single Google Cloud project that is used as an image repository. This project is protected with VPC Service Controls and exists in the perimeter along with other projects in your organization. This lets other projects deploy images from the image repository project. A team requires deploying a third-party disk image that is stored in an external Google Cloud organization. You need to grant read access to the disk image so that it can be deployed into the perimeter.
What should you do?

A. 1. Update the perimeter.
2. Configure the egressTo field to set identityType to ANY_IDENTITY.
3. Configure the egressFrom field to include the external Google Cloud project number as an allowed resource and the serviceName to compute.googleapis.com.
B. 1. Update the perimeter.
2. Configure the egressTo field to include the external Google Cloud project number as an allowed resource and the serviceName to compute.googleapis.com.
3. Configure the egressFrom field to set identityType to ANY_IDENTITY.
C. Allow the external project by using the organizational policy,
constraints/compute.trustedImageProjects.
D. 1. Update the perimeter.
2. Configure the ingressFrom field to set identityType to ANY_IDENTITY.
3. Configure the ingressTo field to include the external Google Cloud project number as an allowed resource and the serviceName to compute.googleapis.com.

Answer: B

Explanation:
A Compute Engine client within a service perimeter calling a Compute Engine create operation where the image resource is outside the perimeter.
https://cloud.google.com/vpc-service-controls/docs/ingress-egress-
rules#:~:text=Egress%20Refers%20to%20any%20access,resource%20is%20outside%20the%20 perimeter.

NEW QUESTION # 201
......

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