Which of the following descriptions of quality aspects of a data set is correct?

The correct answer is A. The quality aspect "Incomplete data" describes the fact that data is missing, e.g., for a certain. 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 as incomplete data, irrelevant data,incorrect data, unbalanced data, or data lacking preprocessing…

Machine Learning (ML)

Question

Options

AThe quality aspect "Incomplete data" describes the fact that data is missing, e.g., for a certain
BThe quality aspect "Data not preprocessed" describes the fact that the collected data was
CThe quality aspect "Irrelevant data" describes the fact that irrelevant data does not affect the ML
DThe quality aspect "Unbalanced data" describes the fact that the data used should be as up-to-

How the community answered

(23 responses)

A
78% (18)
B
13% (3)
C
4% (1)
D
4% (1)

Explanation

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 as incomplete 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.

Topics

#data quality#incomplete data#dataset quality#unbalanced data

Community Discussion

No community discussion yet for this question.

Full CT-AI Practice