Actual Google Professional-Cloud-Security-Engineer Exam Questions in PDF

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

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

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Google Cloud Certified - Professional Cloud Security Engineer Exam Sample Questions (Q199-Q204):

NEW QUESTION # 199
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 Web Security Scanner to validate the usage of an outdated library in the code, and then use a secured version of the included library.
B. Use Cloud IAP based on IP address or end-user device attributes to prevent and fix the vulnerability.
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:
https://cloud.google.com/security-scanner/docs/remediate-findings

NEW QUESTION # 200
Your organization is rolling out a new continuous integration and delivery (CI/CD) process to deploy infrastructure and applications in Google Cloud Many teams will use their own instances of the CI/CD workflow It will run on Google Kubernetes Engine (GKE) The CI/CD pipelines must be designed to securely access Google Cloud APIs What should you do?

A. *1 Create two service accounts one for the infrastructure and one for the application deployment
*2 Use workload identities to let the pods run the two pipelines and authenticate with the service accounts
*3 Run the infrastructure and application pipelines in separate namespaces
B. * 1 Create individual service accounts (or each deployment pipeline
*2 Add an identifier for the pipeline in the service account naming convention
*3 Ensure each pipeline runs on dedicated pods
*4 Use workload identity to map a deployment pipeline pod with a service account
C. *1 Create service accounts for each deployment pipeline
*2 Generate private keys for the service accounts
*3 Securely store the private keys as Kubernetes secrets accessible only by the pods that run the specific deploy pipeline
D. *1 Create a dedicated service account for the CI/CD pipelines
*2 Run the deployment pipelines in a dedicated nodes pool in the GKE cluster
*3 Use the service account that you created as identity for the nodes in the pool to authenticate to the Google Cloud APIs

Answer: B

NEW QUESTION # 201
Which Google Cloud service should you use to enforce access control policies for applications and resources?

A. Google Cloud Armor
B. Shielded VMs
C. Cloud NAT
D. Identity-Aware Proxy

Answer: D

Explanation:
To enforce access control policies for applications and resources in Google Cloud, the recommended service is Identity-Aware Proxy (IAP).
* Identity-Aware Proxy (IAP):
* IAP allows you to control access to your applications and resources based on the identity of the user and the context of the request. It integrates with IAM to provide fine-grained access control, ensuring that only authorized users can access specific resources.
IAP helps enforce security policies at the application layer, providing an additional layer of protection beyond traditional network-based security measures.
References
* Identity-Aware Proxy documentation

NEW QUESTION # 202
Your organization hosts a financial services application running on Compute Engine instances for a third- party company. The third-party company's servers that will consume the application also run on Compute Engine in a separate Google Cloud organization. You need to configure a secure network connection between the Compute Engine instances. You have the following requirements:
* The network connection must be encrypted.
* The communication between servers must be over private IP addresses.
What should you do?

A. Configure an Apigee proxy that exposes your Compute Engine-hosted application as an API, and is encrypted with TLS which allows access only to the third party.
B. Configure a Cloud VPN connection between your organization's VPC network and the third party's that is controlled by VPC firewall rules.
C. Configure a VPC Service Controls perimeter around your Compute Engine instances, and provide access to the third party via an access level.
D. Configure a VPC peering connection between your organization's VPC network and the third party's that is controlled by VPC firewall rules.

Answer: B

Explanation:
To meet the requirements of encrypted communication over private IP addresses between Compute Engine instances in different Google Cloud organizations, a Cloud VPN connection is appropriate:
* Cloud VPN: Cloud VPN creates a secure, encrypted tunnel between your organization's VPC network and the third party's VPC network. This ensures that data transmitted over the network is encrypted and secure.
* Private IP Communication: Cloud VPN allows communication over private IP addresses, which helps maintain security by keeping traffic within the Google Cloud network and not exposing it to the public internet.
* Firewall Rules: VPC firewall rules can be configured to control the traffic that flows through the VPN, ensuring that only authorized traffic is allowed, further enhancing security.
By setting up a Cloud VPN connection, you can achieve secure, encrypted communication over private IP addresses between different Google Cloud organizations.
References
* Cloud VPN Overview

NEW QUESTION # 203
Your organization is developing a sophisticated machine learning (ML) model to predict customer behavior for targeted marketing campaigns. The BigQuery dataset used for training includes sensitive personal information. You must design the security controls around the AI/ML pipeline.
Data privacy must be maintained throughout the model's lifecycle and you must ensure that personal data is not used in the training process. Additionally, you must restrict access to the dataset to an authorized subset of people only. What should you do?

A. Implement at-rest encryption by using customer-managed encryption keys (CMEK) for the pipeline. Implement strict Identity and Access Management (IAM) policies to control access to BigQuery.
B. Deploy the model on Confidential VMs for enhanced protection of data and code while in use.Implement strict Identity and Access Management (IAM) policies to control access to BigQuery.
C. Implement Identity-Aware Proxy to enforce context-aware access to BigQuery and models based on user identity and device.
D. De-identify sensitive data before model training by using Cloud Data Loss Prevention (DLP)APIs.
and implement strict Identity and Access Management (IAM) policies to control access to BigQuery.

Answer: D

Explanation:
Data De-identification: De-identifying sensitive data using Cloud DLP APIs ensures that the data used for model training does not contain personally identifiable information (PII). This protects data privacy and reduces the risk of unauthorized access or misuse.
IAM Policies: Implementing strict IAM policies controls access to BigQuery, ensuring that only authorized personnel can access and use the dataset. This further protects data privacy and reduces the risk of unauthorized access.
Comprehensive Approach: This approach combines data de-identification and IAM controls to provide a robust and effective security solution for the AI/ML pipeline.

NEW QUESTION # 204
......

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