ISTQB CT-AI Exam With Confidence Using Practice Dumps

Clustering is a type of machine learning technique used to group similar data points into clusters. It is a key concept in unsupervised learning, where the algorithm tries to find patterns or groupings in data without prior knowledge of output classes. Let's analyze each option:

A. Clustering is classification of a continuous quantity.

This is incorrect. Classification typically involves discrete categories, whereas clustering involves grouping similar data points. Classification of continuous quantities is generally referred to as regression.

B. Clustering is supervised learning.

This is incorrect. Clustering is an unsupervised learning technique because it does not rely on labeled data.

C. Clustering is done without prior knowledge of output classes.

This is correct. In clustering, the algorithm groups data points into clusters without any prior knowledge of the classes. It discovers the inherent structure in the data.

D. Clustering requires you to know the classes.

This is incorrect. Clustering does not require prior knowledge of classes. Instead, it aims to identify and form the classes or groups based on the data itself.

Therefore, the correct answer isCbecause clustering is an unsupervised learning technique done without prior knowledge of output classes​​.

The syllabus explains that input changes that arein the same domainas what was used for training are expected to be handled with adaptability:

"Adaptability refers to the ability of a system to adjust its behavior in response to changes in its environment or inputs. This includes changes to the inputs which are still within the expected operational range of the system, such as resolution changes in images or sensor data."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.2)

The syllabus defines clustering as:

“Clustering: This is when the problem requires the identification of similarities in input data points that allows them to be grouped based on common characteristics or attributes. For example, clustering is used to categorize different types of customers for the purpose of marketing.”

(Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.2, page 26 of 99)