Which ONE of the following options describes a scenario of A/B testing the LEAST? SELECT ONE OPTION

A/B testing, also known as split testing, is a method used to compare two versions of a product or

system to determine which one performs better. It is widely used in web development, marketing,

and machine learning to optimize user experiences and model performance. Here’s why option C is

the least descriptive of an A/B testing scenario:

Understanding A/B Testing:

In A/B testing, two versions (A and B) of a system or feature are tested against each other. The

objective is to measure which version performs better based on predefined metrics such as user

engagement, conversion rates, or other performance indicators.

Application in Machine Learning:

In ML systems, A/B testing might involve comparing two different models, algorithms, or system

configurations on the same set of data to observe which yields better results.

Why Option C is the Least Descriptive:

Option C describes comparing the performance of an ML system on two different input datasets. This

scenario focuses on the input data variation rather than the comparison of system versions or

features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment

with two versions being tested under the same conditions, not different datasets.

Clarifying the Other Options:

A . A comparison of two different websites for the same company to observe from a user acceptance

perspective: This is a classic example of A/B testing where two versions of a website are compared.

B . A comparison of two different offers in a recommendation system to decide on the more effective

offer for the same users: This is another example of A/B testing in a recommendation system.

D . A comparison of the performance of two different ML implementations on the same input data:

This fits the A/B testing model where two implementations are compared under the same

conditions.

Reference:

ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B

testing in various contexts.

"Understanding A/B Testing" (ISTQB CT-AI Syllabus).