Free Google Professional-Cloud-Security-Engineer Exam Questions

Question 1

A company is running their webshop on Google Kubernetes Engine and wants to analyze customer

transactions in BigQuery. You need to ensure that no credit card numbers are stored in BigQuery

What should you do?

A : eate a BigQuery view with regular expressions matching credit card numbers to query and delete affected rows

B : the Cloud Data Loss Prevention API to redact related infoTypes before data is ingested into BigQuery.

C : verage Security Command Center to scan for the assets of type Credit Card Number in BigQuery

D : able Cloud Identity-Aware Proxy to filter out credit card numbers before storing the logs in BigQuery.

Answer: D

Question 2

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 : Cloud IAP based on IP address or end-user device attributes to prevent and fix the vulnerability.

B : t up an HTTPS load balancer, and then use Cloud Armor for the production environment to prevent the potential XSS attack.

C : Web Security Scanner to validate the usage of an outdated library in the code, and then use a secured version of the included library.

D : Web Security Scanner in staging to simulate an XSS injection attack, and then use a templating system that supports contextual auto-escaping.

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

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

As an Online education platform company, you need to develop an internal App Engine application that can access a user's Google Drive without relying on current user credentials, while following Google's recommended practices. What steps should you take to accomplish this using Google Cloud Services?

A : To impersonate the user, you should create a new service account and provide it G Suite domain-wide delegation, which the application can use.

B : To ensure secure operations of the application, it is recommended to use a separate G Suite Admin account and authenticate the application's activities using these credentials.

C : Generate a fresh Service account and assign Service Account User role to all the users of the application.

D : To grant access to all application users, a new Service account should be created and a Google Group should be created and all application users should be added to this group. Then, grant the Service Account User role to this group.

Answer: A