Question 31
Domain 2 — AI Operations, Lifecycle, and Control EnvironmentWhen conducting A/B testing on two artificial intelligence (AI) model versions, which of the following should be varied to BEST assess their performance impact?
Correct answer: D
Explanation
A/B testing compares two versions by changing the factors that can affect outcomes, so varying the "model architectures and hyperparameters" isolates performance differences between the AI models. These settings directly control how the models learn and make predictions, making them the best variables to assess impact.
Why each option is right or wrong
A. Volume of training data and origin of data sources
B. Length of training time and environment of deployment
C. Methods of visualization and layout of user interface
D. Model architectures and hyperparameters
In an A/B test, the comparison must isolate the independent variables that plausibly drive performance differences, and in AI systems those are the model design choices and training settings. Model architecture and hyperparameters are the parameters that directly control capacity, optimization, and generalization, so changing them between the two versions is the proper way to measure impact while holding the rest of the evaluation constant.