AIF-C01 Machine Learning Operations (MLOps)

Question 1

What is the primary goal of MLOps?

Accepted Answer

To streamline the end-to-end lifecycle of ML models from development to production

Answer

MLOps combines ML, DevOps, and data engineering practices to standardize and automate the full ML lifecycle including building, deploying, and monitoring models.

Question 2

Which type of drift occurs when the statistical properties of the input features change over time?

Accepted Answer

Data drift

Answer

Data drift (also called feature drift or covariate shift) refers to changes in the statistical properties of model input features compared to the training data.

Question 3

What AWS service provides a central repository to catalog, version, and manage ML models?

Accepted Answer

Amazon SageMaker Model Registry

Answer

Amazon SageMaker Model Registry allows teams to catalog models, manage model versions, associate metadata, and control model approval status for deployment.

Question 4

In a CI/CD pipeline for ML, what does 'CT' (Continuous Training) specifically refer to?

Accepted Answer

Automatically retraining models when new data or triggers are detected

Answer

Continuous Training (CT) automatically re-triggers the model training pipeline when new data arrives, performance degrades, or scheduled intervals occur.

Question 5

What is the purpose of a baseline in Amazon SageMaker Model Monitor?

Accepted Answer

To define the expected statistical properties of input data and model outputs for comparison

Answer

A baseline captures statistics and constraints from the training data so Model Monitor can compare live inference data against it to detect drift and violations.

AWS AI Practitioner Practice Test

AWS AI Practitioner Practice Test

AIF-C01 Machine Learning Operations (MLOps)