AIF-C01 Machine Learning Operations (MLOps) 2

Question 1

What is the key difference between a shadow deployment and a canary deployment?

Accepted Answer

Shadow deployment sends real traffic to the new model but discards its predictions

Answer

In shadow deployment, real production requests are mirrored to the new model, but only the existing model's responses are served to users, allowing safe comparison without risk.

Question 2

Amazon SageMaker Feature Store serves what primary purpose in an MLOps workflow?

Accepted Answer

Providing a centralized repository for storing, sharing, and reusing ML features

Answer

SageMaker Feature Store provides a centralized, reusable repository of ML features that ensures consistency between training and inference, and enables feature sharing across teams.

Question 3

What is concept drift in the context of ML model monitoring?

Accepted Answer

A change in the underlying relationship between input features and the target variable

Answer

Concept drift occurs when the statistical relationship between inputs and outputs changes over time, meaning the patterns the model learned are no longer valid.

Question 4

In Amazon SageMaker, what is a 'production variant' used for?

Accepted Answer

Splitting traffic between multiple model versions on a single endpoint

Answer

A production variant allows you to deploy multiple model versions behind a single SageMaker endpoint and control what percentage of traffic each variant receives.

Question 5

Which SageMaker capability automatically detects bias in training data and model predictions?

Accepted Answer

SageMaker Clarify

Answer

SageMaker Clarify provides bias detection for both pre-training data and post-training model predictions, as well as feature importance and model explainability reports.

AWS AI Practitioner Practice Test

AWS AI Practitioner Practice Test

AIF-C01 Machine Learning Operations (MLOps) 2