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

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

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Google Cloud Certified - Professional Cloud Security Engineer Exam Sample Questions (Q299-Q304):

NEW QUESTION # 299
Your organization wants full control of the keys used to encrypt data at rest in their Google Cloud environments. Keys must be generated and stored outside of Google and integrate with many Google Services including BigQuery.
What should you do?

A. Use customer-supplied encryption keys (CSEK) with keys generated on trusted external systems Provide the raw CSEK as part of the API call.
B. Create a Cloud Key Management Service (KMS) key with imported key material Wrap the key for protection during import. Import the key generated on a trusted system in Cloud KMS.
C. Create a KMS key that is stored on a Google managed FIPS 140-2 level 3 Hardware Security Module (HSM) Manage the Identity and Access Management (IAM) permissions settings, and set up the key rotation period.
D. Use Cloud External Key Management (EKM) that integrates with an external Hardware Security Module (HSM) system from supported vendors.

Answer: D

Explanation:
Use Cloud External Key Management (EKM) that integrates with an external Hardware Security Module (HSM) system from supported vendors: Cloud EKM allows you to use encryption keys that are managed externally to Google Cloud. This means you can generate and store your keys in an on-premises HSM or another supported external HSM service, and integrate these keys with various Google Cloud services.
Integration with Google Services: Cloud EKM integrates seamlessly with many Google Cloud services, including BigQuery, Cloud Storage, Compute Engine, and more. This provides you with full control over your encryption keys while still taking advantage of Google Cloud's powerful services.
Reference:
Cloud External Key Management (EKM) documentation
External Key Management overview

NEW QUESTION # 300
Your organization has had a few recent DDoS attacks. You need to authenticate responses to domain name lookups.
Which Google Cloud service should you use?

A. Cloud NAT
B. Cloud DNS with DNSSEC
C. Google Cloud Armor
D. HTTP(S) Load Balancing

Answer: B

NEW QUESTION # 301
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 have 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?

A. Limit external libraries and dependencies that are used for the ML models as much as possible.
Continuously rotate encryption keys that are used to access the user data from BigQuery and Cloud Storage.
B. Thoroughly sanitize all training data prior to model development to reduce risk of poisoning attacks. Use IAM for authorization, and apply role-based restrictions to code repositories and cloud services.
C. Develop strict firewall rules to limit external traffic to Cloud Run instances. Integrate intrusion detection systems (IDS) for real-time anomaly detection on Pub/Sub message flows.
D. Enable container image vulnerability scanning during development and pre-deployment. Enforce Binary Authorization on images deployed from Artifact Registry to your continuous integration and continuous deployment (CVCD) pipeline.

Answer: D

Explanation:
Supply chain risks happen by exploiting vulnerabilities in the images. So scanning the image and blocking deployment secures against supply chain risks. This also matches with the requirement related to the deployment pipeline.

NEW QUESTION # 302
You are developing a new application that uses exclusively Compute Engine VMs Once a day. this application will execute five different batch jobs Each of the batch jobs requires a dedicated set of permissions on Google Cloud resources outside of your application. You need to design a secure access concept for the batch jobs that adheres to the least-privilege principle What should you do?

A. 1. Create a general service account "g-sa" to orchestrate the batch jobs.* 2. Create one service account per batch job Mb-sa-[1-5]," and grant only the permissions required to run the individual batch jobs to the service accounts.* 3. Grant the Service Account Token Creator role to g-sa Use g-sa to obtain short- lived access tokens for b-sa-[1-5] and to execute the batch jobs with the permissions of b-sa-[1-5].
B. * 1. Create a general service account "g-sa" to orchestrate the batch jobs.* 2 Create one service account per batch job 'b-sa-[1-5)\ Grant only the permissions required to run the individual batch jobs to the service accounts and generate service account keys for each of these service accounts* 3. Store the service account keys in Secret Manager. Grant g-sa access to Secret Manager and run the batch jobs with the permissions of b-sa-[1-5].
C. 1. Create a general service account **g-sa" to execute the batch jobs.* 2 Grant the permissions required to execute the batch jobs to g-sa.* 3. Execute the batch jobs with the permissions granted to g-sa
D. 1. Create a workload identity pool and configure workload identity pool providers for each batch job* 2 Assign the workload identity user role to each of the identities configured in the providers.* 3. Create one service account per batch job Mb-sa-[1-5]". and grant only the permissions required to run the individual batch jobs to the service accounts* 4 Generate credential configuration files for each of the providers Use these files to execute the batch jobs with the permissions of b-sa-[1-5].

Answer: A

NEW QUESTION # 303
Last week, a company deployed a new App Engine application that writes logs to BigQuery. No other workloads are running in the project. You need to validate that all data written to BigQuery was done using the App Engine Default Service Account.
What should you do?

A. 1. Use StackDriver Logging and filter on BigQuery Insert Jobs.
2. Click on the email address in line with the App Engine Default Service Account in the authentication field.
3. Click Show Matching Entries.
4. Make sure the resulting list is empty.
B. 1. Go to the IAM section on the project.
2. Validate that the App Engine Default Service Account is the only account that has a role that can write to BigQuery.
Section: (none)
Explanation
C. 1. In BigQuery, select the related dataset.
2. Make sure the App Engine Default Service Account is the only account that can write to the dataset.
D. 1. Use StackDriver Logging and filter on BigQuery Insert Jobs.
2. Click on the email address in line with the App Engine Default Service Account in the authentication field.
3. Click Hide Matching Entries.
4. Make sure the resulting list is empty.

Answer: C

NEW QUESTION # 304
......

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