Free Google Professional-Cloud-Security-Engineer Exam Questions

Question 1

A patch for a vulnerability has been released and a DevOps team in a healthcare technology company needs to update their running containers in Google Kubernetes Engine (GKE). What steps should the DevOps team take to accomplish this task?

A : Confirm whether auto-upgrade is turned on; if enabled, Google will handle node upgrades within a GKE cluster.

B : Set up the containers to automatically upgrade when the base image becomes available in the Container Registry.

C : Apply a patch or update the application code, construct a new image, and deploy it again.

D : Utilize Puppet or Chef to deploy the necessary patch to the container that's currently running.

Answer: C

Question 2

Your organization must comply with the regulation to keep instance logging data within Europe. Yourworkloads will be hosted in the Netherlands in region europe-west4 in a new project. You must configureCloud Logging to keep your data in the country.What should you do?

A : Configure the organization policy constraint gcp.resourceLocations to europe-west4.

B : Set the logging storage region to eurcpe-west4 by using the gcloud CLI logging settings update.

C : Create a new tog bucket in europe-west4. and redirect the _Def auit bucKet to the new bucket.

D : Configure log sink to export all logs into a Cloud Storage bucket in europe-west4.

Answer: D

Question 3

Your organization must comply with the regulation to keep instance logging data within Europe. Yourworkloads will be hosted in the Netherlands in region europe-west4 in a new project. You must configureCloud Logging to keep your data in the country.What should you do?

A : Configure the organization policy constraint gcp.resourceLocations to europe-west4.

B : Set the logging storage region to eurcpe-west4 by using the gcloud CLI logging settings update.

C : Create a new tog bucket in europe-west4. and redirect the _Def auit bucKet to the new bucket.

D : Configure log sink to export all logs into a Cloud Storage bucket in europe-west4.

Answer: D

Question 4

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 5

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