→ A/B testing allows a controlled experiment comparing the performance of two models — the current (A) vs. the candidate (B) — on live data. It’s an industry best practice to validate real-world behavior before full replacement.
C: CI/CD automates deployment but doesn’t evaluate performance differences.
D: Containerization packages the model but doesn't test it comparatively.
Official References:
CompTIA DataX (DY0-001) Study Guide – Section 5.5:“A/B testing is a recommended approach to validate model performance before switching versions in production.”
ML System Operations Guide, Chapter 6:“Use A/B testing to ensure new models outperform baselines before full rollout.”
—
Contribute your Thoughts:
Chosen Answer:
This is a voting comment (?). You can switch to a simple comment. It is better to Upvote an existing comment if you don't have anything to add.
Submit