SecAI+ Secure CI/CD AI Model Deployment Quiz

1. What is the main advantage of implementing policy-as-code in AI deployment?

Automated enforcement of security and compliance rules

Faster model training

Reduced storage requirements

Improved user interface

2. What is the primary benefit of implementing continuous security monitoring in AI deployment?

Early detection and response to security threats

Improved model accuracy only

Reduced infrastructure costs

Faster model training

3. Which compliance standard is most relevant for AI systems handling personal data?

GDPR and data protection regulations

CSS specifications

HTTP standards

File naming conventions

4. In secure AI deployment, what is the purpose of model versioning?

To track changes and enable rollback of compromised versions

To increase storage usage

To slow down deployments

To complicate monitoring

5. Which practice prevents unauthorized access to AI training data in CI/CD pipelines?

Role-based access control (RBAC) and data encryption

Public data sharing

No access restrictions

Manual permission checking

6. What is the role of model explainability in secure AI deployment?

To enable auditing and compliance verification

To increase model size

To reduce inference speed

To simplify user interfaces

7. Which testing phase detects runtime vulnerabilities in deployed AI models?

Dynamic Application Security Testing (DAST)

Unit testing

Integration testing

Smoke testing

8. In secure AI pipelines, what does zero-trust architecture assume?

All traffic is trusted by default

No verification is necessary within networks

Every access request must be verified and authenticated

Only external threats are relevant

9. What is the primary purpose of supply chain security in AI model deployment?

To ensure all dependencies and components are verified and trustworthy

To reduce model inference latency

To improve model accuracy

To decrease deployment costs

10. Which framework is commonly used for securing Kubernetes-based AI deployments?

Pod Security Policy and Network Policies

CSS frameworks

Database indexing

Load balancing algorithms

11. What is the primary purpose of implementing container image scanning in a CI/CD pipeline for AI models?

To reduce deployment time

To detect vulnerabilities and malware before deployment

To increase model accuracy

To optimize storage costs

12. Which security scanning should occur before merging code in AI CI/CD pipelines?

Static Application Security Testing (SAST)

Model performance benchmarking only

User acceptance testing

Load testing

13. In secure AI deployment, what is the purpose of secrets rotation?

To improve model performance

To minimize risk from compromised credentials

To reduce deployment frequency

To simplify monitoring

14. What does TLS provide in AI model API communications?

Data encryption and server authentication

Model versioning only

Automatic model updates

User interface styling

15. Which of the following is essential for compliance in secure AI deployment?

Continuous audit logs and monitoring

Manual security reviews only

Annual penetration testing

Code comments describing security

16. What is the primary benefit of using a private container registry in AI model deployment?

Faster download speeds

Control over image access and vulnerability scanning

Lower storage costs

Automatic model retraining

17. Which encryption approach is recommended for AI model data at rest in secure deployments?

No encryption for performance reasons

AES-256 with key management service

Single-layer encryption

Application-level encryption only

18. In a secure CI/CD pipeline, what is the role of artifact signing?

To verify the integrity and authenticity of deployed artifacts

To compress deployment packages

To encrypt model weights

To log deployment timestamps

19. What does SBOM stand for in the context of secure AI deployment?

System Build Operation Module

Software Bill of Materials

Secure Binary Object Model

System Baseline Operations Manager

20. Which authentication method is most secure for service-to-service communication in AI deployment pipelines?

API keys in environment variables

OAuth 2.0 with short-lived tokens

Basic authentication with username/password

Hardcoded credentials in configuration files

SecAI+ Secure CI CD for AI Model Deployment Quiz