PROFESSIONAL-CLOUD-SECURITY-ENGINEER · Question #335
PROFESSIONAL-CLOUD-SECURITY-ENGINEER Question #335: Real Exam Question with Answer & Explanation
Sign in or unlock PROFESSIONAL-CLOUD-SECURITY-ENGINEER to reveal the answer and full explanation for question #335. The question stem and answer options stay visible for context.
Question
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?
Options
- AEnable container image vulnerability scanning during development and pre-deployment. Enforce
- BThoroughly sanitize all training data prior to model development to reduce risk of poisoning
- CLimit external libraries and dependencies that are used for the ML models as much as possible.
- DDevelop strict firewall rules to limit external traffic to Cloud Run instances. Integrate intrusion
Unlock PROFESSIONAL-CLOUD-SECURITY-ENGINEER to see the answer
You've previewed enough free PROFESSIONAL-CLOUD-SECURITY-ENGINEER questions. Unlock PROFESSIONAL-CLOUD-SECURITY-ENGINEER for full answers, explanations, the timed quiz mode, progress tracking, and the master PDF. Question stem and options stay visible so you can still see what's on the exam.