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 versioning

Answer

Feature engineering

Answer

Data augmentation

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

To store trained model weights

Answer

To monitor model drift in production

Answer

To schedule training jobs

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

Blue-green deployment

Answer

Shadow deployment

Answer

Rolling deployment

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

Training data has errors

Answer

Model weights change during inference

Answer

Feature pipelines run slower

Question 5

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

Accepted Answer

Kubernetes

Answer

Apache Kafka

Answer

Apache Spark

Answer

Airflow

Question 6

In CI/CD for ML, what does a 'model registry' primarily store?

Accepted Answer

Versioned trained models with metadata and lineage

Answer

Raw training datasets

Answer

API keys for cloud services

Answer

Docker container images