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

A model registry provides a single source of truth for model versions, lineage, and stage transitions (staging, production, archived).

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

Tracking predictive performance metrics (accuracy, AUC) on incoming labeled data over time directly reveals concept drift.

Question 3

Shadow mode deployment means:

Accepted Answer

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

Answer

In shadow mode, the new model receives real inputs and generates predictions, but only the existing model's outputs drive actual decisions.

Question 4

Which trigger should initiate a model retraining cycle?

Accepted Answer

Statistically significant performance degradation detected by monitoring

Answer

Retraining should be triggered by evidence of performance degradation, not arbitrary schedules, to ensure resources are spent when needed.

Question 5

Feature stores in production analytics primarily serve to:

Accepted Answer

Provide consistent, reusable feature definitions across training and serving

Answer

Feature stores centralize computed feature logic so training and serving pipelines use identical transformations, preventing training-serving skew.

(CAP) Certified Analytics Professional Practice Test