AI AI Engineer: MLOps and Model Deployment

Question 1

Which MLOps practice ensures that a model retrained on new data maintains or improves its performance compared to the previous version?

Accepted Answer

Model validation and comparison

Answer

Model validation and comparison ensures newly trained models meet or exceed baseline performance before being promoted to production.

Question 2

What is the primary purpose of a feature store in an MLOps pipeline?

Accepted Answer

To provide a centralized repository for reusable, versioned feature data

Answer

A feature store centralizes feature computation and storage so teams can share, reuse, and version features consistently across training and serving.

Question 3

Which deployment strategy releases a new model to a small subset of users before rolling out to everyone?

Accepted Answer

Canary deployment

Answer

Canary deployment directs a small percentage of traffic to the new model, limiting blast radius if issues arise.

Question 4

What does 'data drift' mean in the context of production ML models?

Accepted Answer

The statistical distribution of input features changes over time

Answer

Data drift occurs when the real-world input distribution shifts away from the distribution the model was trained on, degrading performance.

Question 5

Which container orchestration platform is most commonly used to deploy and scale ML model serving workloads?

Accepted Answer

Kubernetes

Answer

Kubernetes automates deployment, scaling, and management of containerized applications, making it the standard for production ML serving.

AI - Engineer Practice Test