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

Section	Weight	Objectives
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. Configuring Binary Authorization for GKE or Cloud Run 4. Automating security scanning for CVEs through CI/CD pipelines
Supporting compliance requirements	14%	- Determining security requirements 1. Identifying security requirements (e.g., regulatory, compliance) 2. Implementing security controls for Vertex AI and AI/ML workloads 3. Configuring audit logging and monitoring (Cloud Audit Logs, Access Transparency)
Configuring access	25%	- Managing Cloud Identity 1. Automating user lifecycle management processes 2. Administering user accounts and groups programmatically 3. Configuring Workforce Identity Federation 4. Managing super administrator accounts 5. Configuring Google Cloud Directory Sync and implementing SSO with a third-party identity provider - Managing service accounts 1. Creating, disabling, and authorizing service accounts 2. Securing, auditing, and mitigating usage of service account keys 3. Securing and protecting service accounts (including default service accounts) 4. Managing and creating short-lived credentials 5. Identifying scenarios requiring service accounts
Ensuring data protection	23%	- Protecting sensitive data and preventing data loss 1. Configuring Sensitive Data Protection (discovering and redacting PII, pseudonymization) 2. Securing secrets with Secret Manager 3. Protecting and managing compute instance metadata 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)

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Google Cloud Certified - Professional Cloud Security Engineer Exam 認定 Professional-Cloud-Security-Engineer 試験問題 (Q148-Q153):

質問 # 148
You have created an OS image that is hardened per your organization's security standards and is being stored in a project managed by the security team. As a Google Cloud administrator, you need to make sure all VMs in your Google Cloud organization can only use that specific OS image while minimizing operational overhead. What should you do? (Choose two.)

A. Set up an image access organization policy constraint, and list the security team managed project in the project's allow list.
B. Store the image in every project that is spun up in your organization.
C. Grant users the compute.imageUser role in the OS image project.
D. Grant users the compute.imageUser role in their own projects.
E. Remove VM instance creation permission from users of the projects, and only allow you and your team to create VM instances.

正解：A、C

解説：
https://cloud.google.com/resource-manager/docs/organization-policy/org-policy-constraints
- constraints/compute.trustedImageProjects
This list constraint defines the set of projects that can be used for image storage and disk instantiation for Compute Engine.
If this constraint is active, only images from trusted projects will be allowed as the source for boot disks for new instances.

質問 # 149
Your company has multiple teams needing access to specific datasets across various Google Cloud data services for different projects. You need to ensure that team members can only access the data relevant to their projects and prevent unauthorized access to sensitive information within BigQuery, Cloud Storage, and Cloud SQL. What should you do?

A. Use VPC Service Controls to create security perimeters around the projects for BigQuery. Cloud Storage, and Cloud SQL services. restricting access based on the network origin of the requests.
B. Grant project-level group permissions by using specific Cloud IAM roles. Use BigQuery authorized views. Cloud Storage uniform bucket-level access, and Cloud SQL database roles.
C. Enable project-level data access logs for BigQuery. Cloud Storage, and Cloud SQL. Configure log sinks to export these logs to Security Command Center to identify unauthorized access attempts.
D. Configure an access level to control access to the Google Cloud console for users managing these data services. Require multi-factor authentication for all access attempts.

正解：B

解説：
Comprehensive and Detailed Explanation From Exact Extract:
This question requires implementing fine-grained data access control across multiple services based on the Principle of Least Privilege.
Project/Service Access (IAM): Granting project-level group permissions with specific Cloud IAM roles (e.g., BigQuery Data Viewer) is the primary way to control who has access to which project's resources.
Data Isolation (Service-Specific): To ensure only relevant data is accessed and to protect sensitive information within the datasets, you must use the most granular control mechanism available for each service:
BigQuery: Authorized Views allow access to specific query results (subsets of data) without granting access to the underlying table.
Cloud Storage: Uniform bucket-level access simplifies and tightens security by forcing all access to be controlled by IAM, preventing accidental object-level exposure.
Cloud SQL: Database Roles are the native, most granular way to control access within the database itself (e.
g., read-only access to specific tables).
Extracts (Conceptual Basis):
"The principle of least privilege dictates that users should only have the permissions necessary to perform their jobs. Granular access is enforced using a combination of IAM roles and service-native access controls." (Source 5.1)
"For BigQuery, using authorized views is the standard way to limit data exposure to users who should only see a subset of data." (Source 5.2)

質問 # 150
You want data on Compute Engine disks to be encrypted at rest with keys managed by Cloud Key Management Service (KMS). Cloud Identity and Access Management (IAM) permissions to these keys must be managed in a grouped way because the permissions should be the same for all keys.
What should you do?

A. Create a single KeyRing for all persistent disks and all Keys in this KeyRing. Manage the IAM permissions at the Key level.
B. Create a KeyRing per persistent disk, with each KeyRing containing a single Key. Manage the IAM permissions at the KeyRing level.
C. Create a single KeyRing for all persistent disks and all Keys in this KeyRing. Manage the IAM permissions at the KeyRing level.
D. Create a KeyRing per persistent disk, with each Keying containing a single Key. Manage the IAM permissions at the Key level.

正解：D

質問 # 151
For compliance reasons, an organization needs to ensure that in-scope PCI Kubernetes Pods reside on "in- scope" Nodes only. These Nodes can only contain the "in-scope" Pods.
How should the organization achieve this objective?

A. Place a taint on the Nodes with the label inscope: true and effect NoSchedule and a toleration to match in the Pod configuration.
B. Create a node pool with the label inscope: true and a Pod Security Policy that only allows the Pods to run on Nodes with that label.
C. Add a nodeSelector field to the pod configuration to only use the Nodes labeled inscope: true.
D. Run all in-scope Pods in the namespace "in-scope-pci".

正解：C

解説：
Explanation
nodeSelector is the simplest recommended form of node selection constraint. You can add the nodeSelector field to your Pod specification and specify the node labels you wantthe target node to have. Kubernetes only schedules the Pod onto nodes that have each of the labels you specify. =>
https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#nodeselector Tolerations are applied to pods. Tolerations allow the scheduler to schedule pods with matching taints. Tolerations allow scheduling but don't guarantee scheduling: the scheduler also evaluates other parameters as part of its function.
=>https://kubernetes.io/docs/concepts/scheduling-eviction/taint-and-toleration/

質問 # 152
A customer wants to make it convenient for their mobile workforce to access a CRM web interface that is hosted on Google Cloud Platform (GCP). The CRM can only be accessed by someone on the corporate network. The customer wants to make it available over the internet. Your team requires an authentication layer in front of the application that supports two-factor authentication Which GCP product should the customer implement to meet these requirements?

A. Cloud Endpoints
B. Cloud Armor
C. Cloud Identity-Aware Proxy
D. Cloud VPN

正解：C

質問 # 153
......

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