AML AML Model Deployment & MLOps

Question 1

What is MLOps in the context of advanced machine learning?

Accepted Answer

The practice of combining ML development with operations to deploy and maintain models in production

Answer

A programming language for machine learning

Answer

A type of neural network architecture

Answer

A dataset versioning tool

Question 2

What is model drift?

Accepted Answer

The degradation of model performance as real-world data distribution changes from training data

Answer

The gradual improvement of a model over time

Answer

The movement of model weights during training

Answer

A regularization technique for production models

Question 3

Which tool is commonly used to containerize ML models for consistent deployment across environments?

Accepted Answer

Docker

Answer

Jupyter Notebook

Answer

NumPy

Answer

Scikit-learn

Question 4

What is a REST API in the context of machine learning model deployment?

Accepted Answer

A standardized interface that allows applications to send data to a model and receive predictions via HTTP

Answer

A database for storing model artifacts

Answer

A monitoring dashboard for deployed ML models

Answer

A version control system for datasets

Question 5

What is A/B testing in the context of ML model deployment?

Accepted Answer

Comparing two model versions by routing live traffic to each and measuring performance differences

Answer

Testing models on two separate servers simultaneously

Answer

Running automated unit tests on model training code

Answer

Testing two different datasets against the same model

Question 6

What does CI/CD stand for in an MLOps pipeline?

Accepted Answer

Continuous Integration / Continuous Deployment

Answer

Containerized Inference / Continuous Development

Answer

Central Intelligence / Cloud Deployment

Answer

Continuous Improvement / Continuous Diagnostics