Free Google Professional-Cloud-Security-Engineer Exam Questions

Question 1

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?

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 : 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 (CI/CD) pipeline.

C : 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.

D : 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.

Answer: B

Question 2

Your company has deployed an application on Compute Engine. The application is accessible by clients on port

587. You need to balance the load between the different instances running the application. The connection

should be secured using TLS, and terminated by the Load Balancer.

What type of Load Balancing should you use?

A : twork Load Balancing

B : TP(S) Load Balancing

C : P Proxy Load Balancing

D : L Proxy Load Balancing

Answer: D

Question 3

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?

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 : 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 (CI/CD) pipeline.

C : 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.

D : 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.

Answer: B

Question 4

You have just created a new log bucket to replace the _Default log bucket. You want to route all log entries that are currently routed to the _Default log bucket to this new log bucket in the most efficient manner. What should you do?

A : Create a user-defined sink with inclusion filters copied from the _Default sink. Select the new log bucket as the sink destination.

B : Create exclusion filters for the _Default sink to prevent it from receiving new logs. Create a userdefined sink, and select the new log bucket as the sink destination.

C : Disable the _Default sink. Create a user-defined sink and select the new log bucket as the sink destination.

D : Edit the _Default sink, and select the new log bucket as the sink destination.

Answer: D

Question 5

You are a consultant for an organization that is considering migrating their data from its private cloud to Google Cloud. The organization's compliance team is not familiar with Google Cloud and needs guidance on how compliance requirements will be met on Google Cloud. One specific compliance requirement is for customer data at rest to reside within specific geographic boundaries. Which option should you recommend for the organization to meet their data residency requirements on Google Cloud?

A : ganization Policy Service constraints

B : ielded VM instances

C : cess control lists

D : olocation access controls

E : ogle Cloud Armor

Answer: A