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

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

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Google Cloud Certified - Professional Cloud Security Engineer Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
You are exporting application logs to Cloud Storage. You encounter an error message that the log sinks don't support uniform bucket-level access policies. How should you resolve this error?

A. Add the roles/logging.logWriter Identity and Access Management (IAM) role to the bucket for the log sink identity.
B. Update your sink with the correct bucket destination.
C. Change the access control model for the bucket
D. Add the roles/logging.bucketWriter Identity and Access Management (IAM) role to the bucket for the log sink identity.

Answer: C

NEW QUESTION # 28
You need to provide a corporate user account in Google Cloud for each of your developers and operational staff who need direct access to GCP resources. Corporate policy requires you to maintain the user identity in a third-party identity management provider and leverage single sign- on. You learn that a significant number of users are using their corporate domain email addresses for personal Google accounts, and you need to follow Google recommended practices to convert existing unmanaged users to managed accounts.
Which two actions should you take? (Choose two.)

A. Send an email to all of your employees and ask those users with corporate email addresses for personal Google accounts to delete the personal accounts immediately.
B. Add users to your managed Google account and force users to change the email addresses associated with their personal accounts.
C. Use Google Cloud Directory Sync to synchronize your local identity management system to Cloud Identity.
D. Use the Google Admin console to view which managed users are using a personal account for their recovery email.
E. Use the Transfer Tool for Unmanaged Users (TTUU) to find users with conflicting accounts and ask them to transfer their personal Google accounts.

Answer: C,E

Explanation:
A - Requires third-party IDp and wants to leverage single sign-on.
D - https://cloud.google.com/architecture/identity/migrating-consumer-
accounts#initiating_a_transfer
"In addition to showing you all unmanaged accounts, the transfer tool for unmanaged users lets you initiate an account transfer by sending an account transfer request."

NEW QUESTION # 29
A company is deploying their application on Google Cloud Platform. Company policy requires long-term data to be stored using a solution that can automatically replicate data over at least two geographic places.
Which Storage solution are they allowed to use?

A. Compute Engine Persistent Disk
B. Cloud BigQuery
C. Compute Engine SSD Disk
D. Cloud Bigtable

Answer: B

Explanation:
https://cloud.google.com/bigquery#:~:text=BigQuery%20transparently%20and%20automatically%20provides,charge%20and%20no%20additional%20setup.&text=BigQuery%20also%20provides%20ODBC%20and,interact%20with%20its%20powerful%20engine.

NEW QUESTION # 30
You are a member of the security team at an organization. Your team has a single GCP project with credit card payment processing systems alongside web applications and data processing systems. You want to reduce the scope of systems subject to PCI audit standards.
What should you do?

A. Use only applications certified compliant with PA-DSS.
B. Move the cardholder data environment into a separate GCP project.
C. Use multi-factor authentication for admin access to the web application.
D. Use VPN for all connections between your office and cloud environments.

Answer: D

Explanation:
Explanation/Reference: https://cloud.google.com/solutions/pci-dss-compliance-in-gcp

NEW QUESTION # 31
You work for a large organization that runs many custom training jobs on Vertex AI. A recent compliance audit identified a security concern. All jobs currently use the Vertex AI service agent. The audit mandates that each training job must be isolated, with access only to the required Cloud Storage buckets, following the principle of least privilege. You need to design a secure, scalable solution to enforce this requirement. What should you do?

A. Use a dedicated service account for each custom training job. Grant each account the storage Legacy Object Reader role for the necessary Cloud Storage buckets.
B. Continue to use the default Vertex AI service agent. Implement VPC Service Controls around the Vertex AI and Cloud Storage services.
C. Create a custom service account. Assign it the storage object user role at the project level. Configure all Vertex AI custom training jobs to run as this service account.
D. Modify the IAM policy of each Cloud Storage bucket to grant the default Vertex AI service agent the storage Legacy Object Reader role.

Answer: A

Explanation:
By default, Vertex AI uses a "Service Agent" (a Google-managed service account) which often has broad permissions across the project. To achieve isolation and least privilege, you must use Custom Service Accounts for each training job.
According to Google Cloud Documentation (Use a custom service account for Vertex AI):
"When you perform custom training, you can configure Vertex AI to use a custom service account. This allows you to grant the job only the specific permissions it needs (e.g., access to a specific Cloud Storage bucket) rather than using the default service agent which may have broader access. This is the recommended practice for multi-tenant or highly regulated environments." Implementation Strategy:
* Identity: Create a unique User-Managed Service Account (UMSA) for each specific training workload or team.
* Permissions: Grant that UMSA the roles/storage.objectViewer (or Legacy Reader) only on the specific bucket containing the training data.
* Job Config: When submitting the Vertex AI job, specify the serviceAccount field in the job request.
Why other options are incorrect:
* A is incorrect: Using one custom service account for all jobs still doesn't provide isolation between the jobs themselves.
* B and C are incorrect: These use the default service agent, which is shared across the project. If one job is compromised or misconfigured, it could potentially access data intended for other jobs because they share the same identity.
Reference:
Google Cloud Documentation: "Use a custom service account" (https://cloud.google.com/vertex-ai/docs
/general/custom-service-account).
Google Cloud Security Engineer Study Guide: Section on "Vertex AI Security Best Practices."

NEW QUESTION # 32
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