ISTQB CT-AI Dumps Questions Answers

The syllabus defines back-to-back testing as a method to compare a modified AI system to the previous version, which is ideal in this scenario:

"Back-to-back testing is performed by comparing the outputs of two systems that are supposed to provide the same outputs, one being a known and trusted system and the other being the test system. This approach can be used to test ML systems after re-training to verify that improvements have not introduced regressions."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 9.3, page 67 of 99)

The ISTQB CT-AI syllabus describes severaldata quality aspectsthat affect ML performance. In Section2.2 – Data Preparation, it explains that datasets may suffer from issues such asincomplete data,irrelevant data,incorrect data,unbalanced data, or data lacking preprocessing. “Incomplete data” means thatportions of the required data are missing, often because some time periods, records, or sources were not captured. This aligns exactly with Option A, which correctly identifies missing intervals as incomplete data.

Option B is incorrect: “data not preprocessed” refers to data that has not undergone normalization, cleaning, or transformation—not data recorded incorrectly. Option C is wrong because irrelevant datadoesnegatively affect ML models by introducing noise and unnecessary features. The syllabus explicitly states that including irrelevant features can degrade model learning. Option D is incorrect: “unbalanced data” relates todisproportionate class distribution, not recency or freshness of data.

Thus, OptionAis the only statement that correctly matches the syllabus definition of this data quality aspect.

A. Feature selection

Feature selection is the process of selecting the most relevant features from the data. While important, it is not directly about handling excess data.

B. Data sampling

Data sampling involves selecting a representative subset of the data for training. When there is more data than needed, sampling can be used to create a manageable dataset that maintains the statistical properties of the full dataset.

C. Data labeling

Data labeling involves annotating data for supervised learning. It is necessary for training models but does not address the issue of having excess data.

D. Data augmentation

Data augmentation is used to increase the size of the training dataset by creating modified versions of existing data. It is useful when there is insufficient data, not when there is excess data.

Therefore, the correct answer isBbecause data sampling is the most relevant activity when dealing with an excess amount of data for training​​.