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

SectionWeightObjectives
Supporting Compliance Requirements11%- Regulatory compliance
  • 1. Controls for GDPR, HIPAA, PCI DSS, ISO 27001
  • 2. Shared responsibility model
- Audit and assessment
  • 1. Evidence collection and reporting
  • 2. Security assessment frameworks
Ensuring Data Protection23%- Encryption implementation
  • 1. Key management and rotation
  • 2. Data loss prevention (DLP)
  • 3. Encryption at rest (CMEK, Google-managed keys)
- Data classification and lifecycle
  • 1. Retention and deletion policies
  • 2. Sensitive data discovery and classification
Configuring Network Security20%- Secure communication
  • 1. Encryption in transit
  • 2. Load balancer security
  • 3. Certificate management
- Perimeter security
  • 1. Identity-Aware Proxy (IAP)
  • 2. VPC design and private access
  • 3. Cloud NGFW rules and policies
Configuring Access25%- Implementing access management
  • 1. User and group management
  • 2. Deny policies and conditional access
  • 3. Service accounts and key management
- Designing access control
  • 1. Identity federation and workload identity
  • 2. IAM roles, permissions, and policies
  • 3. Resource hierarchy and organization policies
Managing Operations19%- Security automation and governance
  • 1. Binary Authorization and supply chain security
  • 2. Policy enforcement and compliance monitoring
  • 3. Infrastructure as Code security
- Security monitoring and logging
  • 1. Threat detection and response
  • 2. Cloud Audit Logs and logging configuration
  • 3. Security Command Center (SCC)

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Google Cloud Certified - Professional Cloud Security Engineer Exam Sample Questions (Q104-Q109):

NEW QUESTION # 104
An employer wants to track how bonus compensations have changed over time to identify employee outliers and correct earning disparities. This task must be performed without exposing the sensitive compensation data for any individual and must be reversible to identify the outlier.
Which Cloud Data Loss Prevention API technique should you use to accomplish this?

Answer: A

Explanation:
Explanation
De-identifying sensitive data Cloud Data Loss Prevention (DLP) can de-identify sensitive data in text content, including text stored in container structures such as tables. De-identification is the process of removing identifying information from data. The API detects sensitive data such as personally identifiable information (PII), and then uses a de-identification transformation to mask, delete, or otherwise obscure the data. For example, de-identification techniques can include any of the following: Masking sensitive data by partially or fully replacing characters with a symbol, such as an asterisk (*) or hash (#). Replacing each instance of sensitive data with a token, or surrogate, string. Encrypting and replacing sensitive data using a randomly generated or pre-determined key. When you de-identify data using the CryptoReplaceFfxFpeConfig or CryptoDeterministicConfig infoType transformations, you can re-identify that data, as long as you have the CryptoKey used to originally de-identify the data. https://cloud.google.com/dlp/docs/deidentify-sensitive-data


NEW QUESTION # 105
You are migrating an on-premises data warehouse to BigQuery Cloud SQL, and Cloud Storage. You need to configure security services in the data warehouse. Your company compliance policies mandate that the data warehouse must:
* Protect data at rest with full lifecycle management on cryptographic keys
* Implement a separate key management provider from data management
* Provide visibility into all encryption key requests
What services should be included in the data warehouse implementation?
Choose 2 answers

Answer: D,E

Explanation:
Customer-Managed Encryption Keys (CMEK):
CMEK allows you to manage encryption keys using Cloud Key Management Service (KMS). This gives you control over the lifecycle of the keys, including rotation, destruction, and auditing.
Set up a Cloud KMS key ring and create encryption keys that will be used to protect your data in BigQuery, Cloud SQL, and Cloud Storage.
Configure the services to use CMEK for encrypting data at rest, ensuring compliance with your organization's security policies.
Cloud External Key Manager (EKM):
Cloud EKM allows you to use keys managed by an external key management provider to encrypt data in Google Cloud services.
Integrate your external key management system with Google Cloud using supported protocols and APIs.
Configure your data warehouse services to use the external keys for encryption, ensuring that key management is handled outside of the Google Cloud environment.
Key Access Justifications:
Enable Key Access Justifications to provide visibility into why encryption keys are being accessed. This helps in monitoring and auditing key usage to ensure compliance and security.
Set up policies and logging to capture and review key access requests, providing insights into how and why keys are used.
Access Transparency and Approval:
Implement Access Transparency to gain visibility into Google's access to your data and encryption keys.
Configure Access Approval to require explicit approval for Google support or engineering access to your data, adding an additional layer of security and control.
Reference:
Customer-Managed Encryption Keys (CMEK)
Cloud External Key Manager (EKM)
Key Access Justifications
Access Transparency
Access Approval


NEW QUESTION # 106
Your organization is building a real-time recommendation engine using ML models that process live user activity data stored in BigQuery and Cloud Storage. Each new model developed is saved to Artifact Registry.
This new system deploys models to Google Kubernetes Engine and uses Pub/Sub for message queues. Recent industry news has been reporting attacks exploiting ML model supply chains. You need to enhance the security in this serverless architecture, specifically against risks to the development and deployment pipeline.
What should you do?

Answer: B

Explanation:
To enhance the security of your machine learning (ML) model supply chain within a serverless architecture, it's crucial to implement measures that protect both the development and deployment pipelines.
* Option A: While limiting external dependencies and rotating encryption keys are good security practices, they do not directly address the risks associated with the ML model supply chain.
* Option B: Implementing container image vulnerability scanning during development and pre- deployment helps identify and mitigate known vulnerabilities in your container images. Enforcing Binary Authorization ensures that only trusted and verified images are deployed in your environment. This combination directly strengthens the security of the ML model supply chain by validating the integrity of container images before deployment.
* Option C: Sanitizing training data and applying role-based access controls are important security practices but do not specifically safeguard the deployment pipeline against compromised container images.
* Option D: While strict firewall rules and intrusion detection systems enhance network security, they do not specifically address vulnerabilities within the container images or the deployment process.
Therefore, Option B is the most effective approach, as it directly addresses the security of the development and deployment pipeline by ensuring that only vetted and secure container images are used in your environment.
References:
* Container Scanning Overview
* Binary Authorization Overview


NEW QUESTION # 107
Your team sets up a Shared VPC Network where project co-vpc-prod is the host project. Your team has configured the firewall rules, subnets, and VPN gateway on the host project. They need to enable Engineering Group A to attach a Compute Engine instance to only the 10.1.1.0/24 subnet.
What should your team grant to Engineering Group A to meet this requirement?

Answer: D

Explanation:
To enable Engineering Group A to attach a Compute Engine instance to a specific subnet (10.1.1.0/24) in a Shared VPC, you should grant the Compute Network User Role at the subnet level. This role allows users to use the subnetwork for their instances without giving them broader permissions at the project level.
Step-by-Step:
* Identify the Subnet: Locate the subnet (10.1.1.0/24) in the host project.
* Grant Role:
* Navigate to the GCP Console > VPC network > VPC networks.
* Select the Shared VPC host project and locate the specific subnet.
* Click on "Edit" and go to the "IAM & Admin" section.
* Assign the "Compute Network User" role to Engineering Group A at the subnet level.
* Verification: Ensure that Engineering Group A can now attach Compute Engine instances to the specified subnet.
References:
* Shared VPC Overview
* Compute Network User Role


NEW QUESTION # 108
Your team wants to make sure Compute Engine instances running in your production project do not have public IP addresses. The frontend application Compute Engine instances will require public IPs. The product engineers have the Editor role to modify resources. Your team wants to enforce this requirement.
How should your team meet these requirements?

Answer: A

Explanation:
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.
Reference:
GCP Organization Policies Documentation
Compute Engine Network Configurations


NEW QUESTION # 109
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