Pass the Google Google Cloud Certified Professional-Cloud-Security-Engineer Questions and answers with CertsForce

This question combines three security requirements: strong isolation (segmentation), secure remote access, and least privilege.

Strong Isolation: Creating separate VPC networks for each tier (C) provides the strongest network isolation/segmentation, limiting the blast radius compared to a single VPC with subnets (B, D). VPC peering is the standard way to allow controlled communication between these separate VPCs.

Extract: "Isolate sensitive data in its own VPC network." (Source 2.5) Segmentation via separate VPCs is a standard best practice for isolating sensitive workloads.

Secure Remote Access and Least Privilege: Identity-Aware Proxy (IAP) is the recommended Google Cloud service to provide secure remote access to virtual machine instances without requiring a public IP or VPN, which aligns with the zero-trust principle of explicit validation and least privilege by verifying user identity and context. Granting SSH keys and root access (A) or the Network Admin role (B) or Project Ownership (D) violates the principle of least privilege.

Extract: "Access control: Enforce access controls based on user identity and context by using solutions like... Identity-Aware Proxy (IAP). By doing this, you shift security from the network perimeter to individual users and devices. This approach enables granular access control and reduces the attack surface." (Source 2.2)

Extract: "BeyondCorp uses Google Cloud tools, such as... and Identity-Aware Proxy, to push the perimeter from the network to individual devices and users." (Source 2.3)

Extract: "IAP protects GCP-hosted applications by verifying user identity and context before granting access... When you grant a user access to an application or resource by IAP, they're subject to the fine-grained access controls implemented by the product in use without requiring a VPN." (Source 2.3)

Option C is the only one that satisfies all three requirements by using separate VPCs (strong isolation) and IAP (secure remote access with least privilege).

Security Command Center (SCC) in Google Cloud provides several features to help organizations detect and respond to security threats and misconfigurations.

Event Threat Detection: This feature continuously monitors and analyzes system logs to detect potential threats such as crypto mining. It uses machine learning and threat intelligence to identify suspicious activities and generate alerts.

Security Health Analytics: This feature helps identify common misconfigurations and compliance violations that could impact security. It provides visibility into security posture and helps remediate issues related to misconfigurations in your Google Cloud environment.

By using both Event Threat Detection and Security Health Analytics, you can effectively monitor for crypto mining activities and detect common misconfigurations that could compromise security.

References

Security Command Center Documentation

Event Threat Detection

Security Health Analytics

The key requirements are maintaining a specific set of controls, including data residency and personnel data access controls, specifically to meet a highly regulated industry's compliance program.

Assured Workloads is the specific Google Cloud service designed to help customers meet these stringent regulatory and compliance requirements (e.g., FINRA, FedRAMP, HIPAA, etc.) by enforcing a specific control bundle.

Extracts:

"Assured Workloads helps organizations run their sensitive workloads securely and in a compliant manner... by providing continuous compliance monitoring for specific compliance programs." (Source 4.1)

"When you create an Assured Workloads folder, the platform automatically enforces compliance controls... including: Data residency and location controls... Personnel access controls... Organizational policies..." (Source 4.2)

This service creates a dedicated, compliant environment in a folder, ensuring the necessary configurations and personnel controls are applied automatically and continuously maintained, which is a more complete solution than the posture management in option B (which focuses only on configuration monitoring) or the singular organizational policy in option C.

The Organization Policy Service provides a specific constraint called constraints/gcp.restrictServiceUsage. This is the most direct and efficient way to "Allow" or "Deny" specific Google Cloud APIs (services) at any node in the hierarchy (Org, Folder, or Project).

According to Google Cloud Documentation (Restricting Resource Service Usage):

"The Restrict Resource Service Usage constraint allows enterprise administrators to control which Google Cloud services can be used within their Google Cloud resource hierarchy. This constraint can be enforced on an Organization, Folder, or Project. When applied at the folder level, it restricts service usage for all descendant projects within that folder."

Why Option C is the most efficient:

Targeted: By applying it directly to the folder, you don't need to manage complex "exceptions" at the Org level (as in Option B).

Simple: It is a single policy rule that defines an "Allowlist" of services.

IAM vs. Org Policy: IAM (Option A) controls who can do something, but Org Policy controls what can be done. Even if a user is an Editor, the Org Policy will block them from enabling a restricted API.

VPC-SC (Option D): VPC Service Controls is much more complex and is used for data exfiltration prevention, not for simply restricting which APIs can be enabled/used in a project.

Objective: Ensure only front-end Compute Engine instances have public IPs, while others do not.

Solution: Use an organization policy to enforce this requirement.

Steps:

Step 1: Open the Google Cloud Console.

Step 2: Navigate to the Organization Policies page.

Step 3: Create a new policy with the constraint constraints/compute.requireOsLogin (or similar constraint to manage public IPs).

Step 4: Define the conditions to allow public IPs only for the front-end instances.

Step 5: Apply the policy to the organization or specific projects as necessary.

By setting up an organization policy with specific conditions, you can control which instances are allowed to have public IPs based on their role or other attributes.

Setting up separate service perimeters for dev, staging, and prod environments allows for more granular control and monitoring. Automating the addition of new projects to the respective perimeters ensures that all projects are consistently secured without manual intervention.

Steps:

Set Up Service Perimeters: Use Access Context Manager to define and configure three separate service perimeters for dev, staging, and prod.

Deploy Monitoring Function: Create a Cloud Function that monitors the "implementation" folder for new projects using Stackdriver (Cloud Monitoring) and Cloud Pub/Sub.

Automate Perimeter Updates: Configure the Cloud Function to execute Terraform scripts that automatically add new projects to the appropriate service perimeter.

The problem states that the organization is using Model Garden and needs to ensure users can only access approved models. This implies a need for a central, enforceable control mechanism.

Organization Policies and Constraints: Google Cloud Organization Policy Service allows administrators to centrally control resources across an organization. Constraints are specific types of restrictions that can be applied. For AI Platform (which includes Vertex AI and Model Garden), there are specific constraints designed to control model usage.

vertexai.allowedModels Constraint: This specific organization policy constraint is designed precisely to restrict which models can be used within a given organization, folder, or project. It provides a centralized way to define a list of approved models that users are allowed to access.Extract Reference: "The vertexai.allowedModels constraint allows you to specify a list of model URIs that are allowed to be used within the resource hierarchy." and "This constraint helps organizations enforce compliance and control which models are consumed by their users." (Google Cloud documentation, typically found under Organization Policy Service constraints for Vertex AI or AI Platform)

Let's evaluate the other options:

A. Configure IAM permissions on individual Model Garden to restrict access to specific models: IAM (Identity and Access Management) typically grants permissions at a broader resource level (e.g., project, dataset, model resource). While you can control who can manage models, directly restricting access to specific models within Model Garden for consumption via IAM roles on individual models is not the primary mechanism for enforcing a list of approved models across an organization in a preventative way. Organization policies are designed for this kind of broad, preventative control.

B. Regularly audit user activity logs in Vertex AI to identify and revoke access to unapproved models: Auditing logs is a reactive measure. While important for monitoring and detecting violations, it does not prevent users from accessing unapproved models in the first place. The requirement is to ensure they can only access approved models, implying a proactive control.

C. Train custom models within your Vertex AI project and restrict user access to these models: This is about managing access to custom-trained models, not about controlling access to the collection of models in Model Garden, which often includes pre-trained or publicly available models that need to be whitelisted. It doesn't address the requirement of ensuring users only access approved models from the broader Model Garden collection.

Therefore, implementing an organization policy with the vertexai.allowedModels constraint is the most effective and Google-recommended way to centrally ensure that users can only access approved models within an organization using Model Garden.

Physical Token for MFA: Implement multi-factor authentication (MFA) using physical tokens (such as security keys) for super admin accounts. This adds an extra layer of security to the highest privilege accounts.

Non-Privileged Identities: Provide super admins with separate non-privileged accounts for daily activities. This practice minimizes the risk associated with using highly privileged accounts for routine tasks.

Account Management: Ensure that super admin accounts are only used for tasks requiring elevated privileges, reducing exposure to potential security threats. These measures enhance the security of super admin accounts, protecting your Google Cloud organization from unauthorized access. References:

Google Cloud - Best Practices for Securing Cloud Identity

Google Cloud - Using Security Keys

Admin activity logs are always created to log entries for API calls or other actions that modify the configuration or metadata of resources. For example, these logs record when users create VM instances or change Identity and Access Management permissions.

To allow Compute Engine instances to access public repositories for security updates while an egress firewall rule is in place to deny all internet traffic, you need to create a more specific egress rule that permits traffic to the CIDR range of the repository. The priority of this rule should be lower (i.e., a higher priority number) than the deny rule.

Steps:

Identify the CIDR Range: Determine the CIDR range of the public repository from which the security updates will be fetched.

Create Egress Firewall Rule: Create a new egress firewall rule allowing traffic to the identified CIDR range with a priority less than 1000.

Apply Firewall Rule: Use the Google Cloud Console or gcloud command-line tool to apply the new firewall rule.