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Google Professional-Cloud-Security-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring data protection	23%	- Protecting sensitive data and preventing data loss 1. Securing secrets with Secret Manager 2. Protecting and managing compute instance metadata 3. Configuring Sensitive Data Protection (discovering and redacting PII, pseudonymization) 4. Restricting access to Google Cloud data services (BigQuery, Cloud Storage, Cloud SQL)
Configuring network security	19%	- Designing network security 1. Configuring network perimeter controls (firewall rules, hierarchical firewall policies, Cloud NGFW) 2. Configuring load balancing for security (Cloud Armor, SSL policies) 3. Using Cloud NAT to enable outbound traffic 4. Establishing private connectivity between VPC and Google APIs (Private Google Access, Private Service Connect)
Managing operations	19%	- Automating infrastructure and application security 1. Automating virtual machine and container image creation (hardening, maintenance, patch management) 2. Managing policy and drift detection at scale (CSPM, custom org policies, Security Health Analytics) 3. Automating security scanning for CVEs through CI/CD pipelines 4. Configuring Binary Authorization for GKE or Cloud Run
Configuring access	25%	- Managing Cloud Identity 1. Automating user lifecycle management processes 2. Administering user accounts and groups programmatically 3. Configuring Google Cloud Directory Sync and implementing SSO with a third-party identity provider 4. Configuring Workforce Identity Federation 5. Managing super administrator accounts - Managing service accounts 1. Securing, auditing, and mitigating usage of service account keys 2. Managing and creating short-lived credentials 3. Creating, disabling, and authorizing service accounts 4. Securing and protecting service accounts (including default service accounts) 5. Identifying scenarios requiring service accounts
Supporting compliance requirements	14%	- Determining security requirements 1. Implementing security controls for Vertex AI and AI/ML workloads 2. Identifying security requirements (e.g., regulatory, compliance) 3. Configuring audit logging and monitoring (Cloud Audit Logs, Access Transparency)

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Google Cloud Certified - Professional Cloud Security Engineer Exam Sample Questions (Q289-Q294):

NEW QUESTION # 289
You are creating an internal App Engine application that needs to access a user's Google Drive on the user's behalf. Your company does not want to rely on the current user's credentials. It also wants to follow Google- recommended practices.
What should you do?

A. Create a new Service account, and give all application users the role of Service Account User.
B. Create a new service account, and grant it G Suite domain-wide delegation. Have the application use it to impersonate the user.
C. Use a dedicated G Suite Admin account, and authenticate the application's operations with these G Suite credentials.
D. Create a new Service account, and add all application users to a Google Group. Give this group the role of Service Account User.

Answer: B

Explanation:
To access a user's Google Drive on their behalf without relying on the user's credentials and following Google- recommended practices, you should use a service account with domain-wide delegation.
* Create a Service Account:
* Go to the Cloud Console, navigate to IAM & Admin > Service Accounts.
* Click "Create Service Account" and provide necessary details.
* Grant Domain-Wide Delegation:
* Edit the service account to enable "G Suite Domain-wide Delegation".
* Download the JSON key file.
* Configure API Access in G Suite:
* Go to the Google Admin Console.
* Navigate to Security > API Controls > Domain-wide Delegation.
* Add a new API client and use the client ID from the service account.
* Authorize the necessary API scopes (e.g., https://www.googleapis.com/auth/drive).
* Implement in Application:
* Use the Google API Client Library for the desired language.
* Load the service account credentials and perform user impersonation to access Google Drive.
References:
* Domain-wide Delegation of Authority
* Using OAuth 2.0 for Server to Server Applications

NEW QUESTION # 290
You are running a workload which processes very sensitive data that is intended to be used downstream by data scientists to train further models. The security team has very strict requirements around data handling and encryption, approved workloads, as well as separation of duties for the users of the output of the workload.
You need to build the environment to support these requirements. What should you do?

A. Use Dataflow with Confidential Computing enabled to process the data and stream the results to a CMEK encrypted Cloud Storage bucket. Assign a storage object viewer role to the data scientist service account. Manage access to this service account by using Workload Identity pools.
B. Use Dataproc with Confidential Computing enabled to process the data and stream the results to a CMEK encrypted Cloud Storage bucket. Assign a storage object reader role to the data scientist service account. Manage access to this service account by using Workload Identity pools.
C. Use Confidential Computing within Confidential Space, assign workload operator roles to the confidential compute VM service account. Assign the data collaborator role to the data scientist service account. Manage user access to these service accounts by using attestations and Workload Identity pools.
D. Use Confidential Computing on an N2D VM instance to process that data and output the results to a CMEK encrypted Cloud Storage bucket. Assign a storage object reader role to the data scientist service account. Manage access to this service account by using Workload Identity pools.

Answer: C

Explanation:
For scenarios involving multi-party collaboration or highly sensitive data where even the "Workload Operator" should not see the data, Confidential Space is the specific Google Cloud solution. It builds on Confidential Computing but adds a software framework for "Trustless" data processing.
According to Google Cloud Documentation (Confidential Space Overview):
"Confidential Space provides a secure environment for multiple parties to share sensitive data without any party (including the cloud provider or the project administrator) being able to see the data or the code being executed. It uses attestations to verify the integrity of the workload and Workload Identity Federation to release encryption keys or access only to verified, authorized workloads." Key components of the solution:
* Separation of Duties: Confidential Space explicitly defines roles for the Data Collaborator (owns data), Workload Author (writes code), and Workload Operator (runs infrastructure). The operator can manage the VM but cannot access the memory or the data.
* Attestation: The system uses hardware-rooted attestation to ensure that the code running in the environment is exactly what was approved.
* Output Protection: The processed results can be written to a bucket where only the authorized "Data Scientist" (Data Collaborator role) can retrieve them.
Why other options are incorrect:
* A, C, and D are incorrect: While they use Confidential VMs (which encrypt data in RAM), they do not inherently provide the attestation-based trust framework or the rigorous separation of duties required to prevent a VM administrator (operator) from potentially accessing the data via standard VM management tools or during the ingestion phase. Confidential Space is the only one that mandates a signed "attestation" before granting access to data.
Reference:
Google Cloud Documentation: "Confidential Space overview" (https://cloud.google.com/confidential- computing/confidential-space/docs/confidential-space-overview).
Google Cloud Security Engineer Study Guide: Section on "Confidential Computing and Trusted Execution Environments."

NEW QUESTION # 291
You are implementing communications restrictions for specific services in your Google Cloud organization. Your data analytics team works in a dedicated folder. You need to ensure that access to BigQuery is controlled for that folder and its projects. The data analytics team must be able to control the restrictions only at the folder level. What should you do?

A. Create a scoped policy on the folder with a service perimeter to restrict BigQuery access. Assign the data analytics team the Access Context Manager Editor role on the scoped policy to allow the team to configure the scoped policy.
B. Define a hierarchical firewall policy on the folder to deny BigQuery access. Assign the data analytics team the Compute Organization Firewall Policy Admin role to allow the team to configure rules for the firewall policy.
C. Create an organization-level access policy with a service perimeter to restrict BigQuery access.
Assign the data analytics team the Access Context Manager Editor role on the access policy to allow the team to configure the access policy.
D. Enforce the Restrict Resource Service Usage organization policy constraint on the folder to restrict BigQuery access. Assign the data analytics team the Organization Policy Administrator role to allow the team to manage exclusions within the folder.

Answer: A

Explanation:
Scoped Policy: A scoped policy allows you to apply restrictions specifically to a folder and its projects Service Perimeter: By using a service perimeter, you can define which services (like BigQuery) can be accessed from within the specified folder.

NEW QUESTION # 292
How should a customer reliably deliver Stackdriver logs from GCP to their on-premises SIEM system?

A. Configure Organizational Log Sinks to export logs to a Cloud Pub/Sub Topic, which will be sent to the SIEM via Dataflow.
B. Configure every project to export all their logs to a common BigQuery DataSet, which will be queried by the SIEM system.
C. Build a connector for the SIEM to query for all logs in real time from the GCP RESTful JSON APIs.
D. Send all logs to the SIEM system via an existing protocol such as syslog.

Answer: A

Explanation:
Scenarios for exporting Cloud Logging data: Splunk This scenario shows how to export selected logs from Cloud Logging to Pub/Sub for ingestion into Splunk. Splunk is a security information and event management (SIEM) solution that supports several ways of ingesting data, such as receiving streaming data out of Google Cloud through Splunk HTTP Event Collector (HEC) or by fetching data from Google Cloud APIs through Splunk Add-on for Google Cloud. Using the Pub/Sub to Splunk Dataflow template, you can natively forward logs and events from a Pub/Sub topic into Splunk HEC. If Splunk HEC is not available in your Splunk deployment, you can use the Add-on to collect the logs and events from the Pub/Sub topic. https://cloud.
google.com/solutions/exporting-stackdriver-logging-for-splunk

NEW QUESTION # 293
An organization adopts Google Cloud Platform (GCP) for application hosting services and needs guidance on setting up password requirements for their Cloud Identity account. The organization has a password policy requirement that corporate employee passwords must have a minimum number of characters.
Which Cloud Identity password guidelines can the organization use to inform their new requirements?

A. Set the minimum length for passwords to be 10 characters.
B. Set the minimum length for passwords to be 6 characters.
C. Set the minimum length for passwords to be 12 characters.
D. Set the minimum length for passwords to be 8 characters.

Answer: D

Explanation:
The minimum length for passwords in Cloud Identity can be set to 8 characters. This aligns with common security best practices for password policies, ensuring a basic level of complexity and security.
Step-by-Step:
Access Admin Console: Log in to the Google Admin console.
Navigate to Security Settings: Go to Security > Password Management.
Set Minimum Length: Set the minimum length for passwords to 8 characters.
Save Changes: Save the settings and ensure that all user accounts adhere to the new policy.
Reference:
Google Cloud Identity Security Settings
Password Policy Best Practices

NEW QUESTION # 294
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