CAP CAP Model Deployment & Lifecycle Management 2

Question 1

What is the primary purpose of a model registry in an MLOps pipeline?

Accepted Answer

To centrally track model versions, metadata, and deployment status

Answer

To store raw input data

Answer

To replace the data warehouse

Answer

To schedule data ingestion jobs

Question 2

Which metric directly measures concept drift in a deployed classification model?

Accepted Answer

Accuracy or AUC degradation over time on live labels

Answer

Server response time

Answer

Number of API calls per minute

Answer

Disk I/O throughput

Question 3

Shadow mode deployment means:

Accepted Answer

Running a new model in parallel without using its outputs for decisions

Answer

Deploying a model only at night

Answer

Hiding model predictions from users

Answer

Encrypting model weights

Question 4

Which trigger should initiate a model retraining cycle?

Accepted Answer

Statistically significant performance degradation detected by monitoring

Answer

A scheduled calendar reminder regardless of performance

Answer

A request from the marketing team for a new dashboard

Answer

Completion of a quarterly budget cycle

Question 5

Feature stores in production analytics primarily serve to:

Accepted Answer

Provide consistent, reusable feature definitions across training and serving

Answer

Store raw unprocessed data permanently

Answer

Replace relational databases

Answer

Generate model visualizations

Question 6

Which practice reduces training-serving skew in deployed models?

Accepted Answer

Sharing a single feature transformation pipeline for both training and serving

Answer

Using different preprocessing code in training and inference pipelines

Answer

Ignoring validation datasets

Answer

Retraining on test data