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

Section	Weight	Objectives
Configuring Access	25%	- Implementing access management 1. User and group management 2. Service accounts and key management 3. Deny policies and conditional access - Designing access control 1. Resource hierarchy and organization policies 2. IAM roles, permissions, and policies 3. Identity federation and workload identity
Managing Operations	19%	- Security monitoring and logging 1. Security Command Center (SCC) 2. Threat detection and response 3. Cloud Audit Logs and logging configuration - Security automation and governance 1. Binary Authorization and supply chain security 2. Policy enforcement and compliance monitoring 3. Infrastructure as Code security
Configuring Network Security	20%	- Secure communication 1. Load balancer security 2. Certificate management 3. Encryption in transit - Perimeter security 1. VPC design and private access 2. Identity-Aware Proxy (IAP) 3. Cloud NGFW rules and policies
Ensuring Data Protection	23%	- Data classification and lifecycle 1. Retention and deletion policies 2. Sensitive data discovery and classification - Encryption implementation 1. Key management and rotation 2. Encryption at rest (CMEK, Google-managed keys) 3. Data loss prevention (DLP)
Supporting Compliance Requirements	11%	- Audit and assessment 1. Evidence collection and reporting 2. Security assessment frameworks - Regulatory compliance 1. Shared responsibility model 2. Controls for GDPR, HIPAA, PCI DSS, ISO 27001

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Google Cloud Certified - Professional Cloud Security Engineer Exam Sample Questions (Q170-Q175):

NEW QUESTION # 170
You are migrating an application into the cloud The application will need to read data from a Cloud Storage bucket. Due to local regulatory requirements, you need to hold the key material used for encryption fully under your control and you require a valid rationale for accessing the key material.
What should you do?

A. Generate a key in your on-premises environment and store it in a Hardware Security Module (HSM) that is managed on-premises Use this key as an external key in the Cloud Key Management Service (KMS). Activate Key Access Justifications (KAJ) and set the external key system to reject unauthorized accesses.
B. Encrypt the data in the Cloud Storage bucket by using Customer Managed Encryption Keys. Configure an 1AM deny policy for unauthorized groups
C. Generate a key in your on-premises environment to encrypt the data before you upload the data to the Cloud Storage bucket Upload the key to the Cloud Key Management Service (KMS). Activate Key Access Justifications (KAJ) and have the external key system reject unauthorized accesses.
D. Encrypt the data in the Cloud Storage bucket by using Customer Managed Encryption Keys backed by a Cloud Hardware Security Module (HSM). Enable data access logs.

Answer: A

Explanation:
By generating a key in your on-premises environment and storing it in an HSM that you manage, you're ensuring that the key material is fully under your control. Using the key as an external key in Cloud KMS allows you to use the key with Google Cloud services without having the key stored on Google Cloud.
Activating Key Access Justifications (KAJ) provides a reason every time the key is accessed, and you can configure the external key system to reject unauthorized access attempts.

NEW QUESTION # 171
A DevOps team will create a new container to run on Google Kubernetes Engine. As the application will be internet-facing, they want to minimize the attack surface of the container.
What should they do?

A. Delete non-used versions from Container Registry.
B. Use Cloud Build to build the container images.
C. Use a Continuous Delivery tool to deploy the application.
D. Build small containers using small base images.

Answer: D

Explanation:
Small containers usually have a smaller attack surface as compared to containers that use large base images. https://cloud.google.com/blog/products/gcp/kubernetes-best-practices-how-and-why-to-build-small-container-images

NEW QUESTION # 172
Your company has deployed an artificial intelligence model in a central project. This model has a lot of sensitive intellectual property and must be kept strictly isolated from the internet. You must expose the model endpoint only to a defined list of projects in your organization. What should you do?

A. Create a central project to host Shared VPC networks that are provided to all other projects. Centrally administer all firewall rules in this project to grant access to the model.
B. Within the model project, create an internal Application Load Balancer that points to the model endpoint. Expose this load balancer with Private Service Connect to a configured list of projects.
C. Within the model project, create an external Application Load Balancer that points to the model endpoint. Create a Cloud Armor policy to restrict IP addresses to Google Cloud.
D. Activate Private Google Access in both the model project and in each project that needs to connect to the model. Create a firewall policy to allow connectivity to Private Google Access addresses.

Answer: B

Explanation:
To share a service (like an AI model) across project boundaries privately and securely-without complex VPC peering or exposing it to the internet-Private Service Connect (PSC) is the recommended solution.14 According to Google Cloud Documentation (Private Service Connect for Vertex AI/Custom Services):
"Private Service Connect allows a service producer to expose a service (via an Internal Load Balancer) to service consumers in other VPC networks or projects.15 The producer creates a Service Attachment that points to the load balancer.16 The consumer creates a PSC Endpoint in their own VPC. Traffic stays entirely within the Google backbone, and the producer can maintain an 'Allowlist' of Project IDs that are permitted to connect." Why this fits the requirements:
* Isolation: The model remains in its own project and is not reachable via the internet (Option C is out).
* Granular Control: PSC allows you to explicitly list which Project IDs can access the service attachment (Option A).17
* Scalability: It avoids the "transitive peering" and "IP overlap" limitations often found in Shared VPC or Peering architectures (Option B).
Reference:
Google Cloud Documentation: "Private Service Connect overview" (https://cloud.google.com/vpc/docs
/private-service-connect).
Vertex AI Documentation: "Using Private Service Connect for online inference" (https://cloud.google.com
/vertex-ai/docs/predictions/private-service-connect).

NEW QUESTION # 173
An organization's typical network and security review consists of analyzing application transit routes, request handling, and firewall rules. They want to enable their developer teams to deploy new applications without the overhead of this full review.
How should you advise this organization?

A. Route all VPC traffic through customer-managed routers to detect malicious patterns in production.
B. All production applications will run on-premises. Allow developers free rein in GCP as their dev and QA platforms.
C. Mandate use of infrastructure as code and provide static analysis in the CI/CD pipelines to enforce policies.
D. Use Forseti with Firewall filters to catch any unwanted configurations in production.

Answer: C

Explanation:
Explanation

NEW QUESTION # 174
You are on your company's development team. You noticed that your web application hosted in staging on GKE dynamically includes user data in web pages without first properly validating the inputted data. This could allow an attacker to execute gibberish commands and display arbitrary content in a victim user's browser in a production environment.
How should you prevent and fix this vulnerability?

A. Use Cloud IAP based on IP address or end-user device attributes to prevent and fix the vulnerability.
B. Use Web Security Scanner to validate the usage of an outdated library in the code, and then use a secured version of the included library.
C. Set up an HTTPS load balancer, and then use Cloud Armor for the production environment to prevent the potential XSS attack.
D. Use Web Security Scanner in staging to simulate an XSS injection attack, and then use a templating system that supports contextual auto-escaping.

Answer: D

Explanation:
Explanation/Reference: https://cloud.google.com/security-scanner/docs/remediate-findings

NEW QUESTION # 175
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