SecAI+ Secure AI Development and MLSecOps

Question 1

What is MLSecOps primarily focused on?

Accepted Answer

Integrating security practices into the ML development lifecycle

Answer

MLSecOps integrates security practices throughout the machine learning development lifecycle, from data collection through model deployment and monitoring.

Question 2

Which phase of the ML pipeline is most vulnerable to data poisoning attacks?

Accepted Answer

Training data collection and preparation

Answer

Training data collection and preparation is most vulnerable because poisoned data injected at this phase corrupts the model's learning and produces malicious behavior at scale.

Question 3

What is the primary purpose of model versioning in a secure ML pipeline?

Accepted Answer

To track changes and enable rollback if a model is compromised

Answer

Model versioning enables teams to track all changes over time and roll back to a previous, uncompromised version if a security incident is discovered in the current deployment.

Question 4

Which practice best ensures the integrity of ML artifacts during a CI/CD build pipeline?

Accepted Answer

Cryptographic signing and hash verification of model files

Answer

Cryptographic signing and hash verification ensures ML artifacts have not been tampered with during storage, transfer, or the build process — the same principle used in secure software supply chains.

Question 5

What does a 'model card' document primarily provide in secure AI development?

Accepted Answer

Transparency about a model's capabilities, limitations, and intended use cases

Answer

Model cards provide transparency about training data, intended uses, known limitations, and potential biases, supporting responsible deployment and security review processes.

CompTIA SecAI+ Practice Test

CompTIA SecAI+ Practice Test

SecAI+ Secure AI Development and MLSecOps