ISTQB CT-AI Dumps Questions Answers

Back-to-back testing is a method where the same set of tests are run on multiple implementations of the system to compare their outputs. This type of testing is typically used to ensure consistency and correctness by comparing the outputs of different implementations under identical conditions. Let's analyze the options given:

A. Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.

This option describes a scenario where two different implementations of the same type of model are being compared using the same dataset. This is a typical back-to-back testing situation.

B. Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for the same data.

This option involves comparing a custom implementation with a standard implementation, which is also a typical back-to-back testing scenario to validate the custom model against a known benchmark.

C. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.

This option involves comparing two different types of models (a neural network and a decision tree). This is not a typical scenario for back-to-back testing because the models are inherently different and would not be expected to produce identical results even on the same data.

D. Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.

This option involves comparing the outputs of the same model on slightly different datasets. This could be seen as a form of robustness testing or sensitivity analysis, but not typical back-to-back testing as it doesn’t involve comparing multiple implementations.

Based on this analysis, optionCis the one that describes a situation of back-to-back testing the least because it compares two fundamentally different models, which is not the intent of back-to-back testing.

The syllabus lists multiple reasons for mislabelled data, including the lack of domain knowledge:

"Lack of required domain knowledge may lead to incorrect labelling."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 4.5.2, page 38 of 99)

TheAI Effectis clearly defined in theISTQB Certified Tester AI Testing Syllabus v1.0under Section1.1 – Definition of AI and AI Effect. The document explains that society’s understanding of what qualifies as “AI” changes over time. Technologies once considered AI—such as expert systems from the 1970s and 1980s or early chess-playing systems—are no longer viewed as AI today. This phenomenon is explicitly labeled the“AI Effect,”described as“the changing perception of what constitutes AI.”The syllabus states that as AI capabilities become routine or widely implemented, they often stop being perceived as true artificial intelligence .

Options B, C, and D do not capture this definition. While AI learning from data (B) is a property of ML, it does not describe the shifting perception of AI. Option C describes a technological achievement, not a perceptual shift. Option D references a historical AI milestone (Deep Blue defeating Kasparov) that the syllabus specifically uses as an example of technology that is no longer considered AI due to the AI Effect. Therefore, onlyOption Aaccurately reflects the AI Effect as defined by the ISTQB syllabus.