DTSA 5504 DATA MINING PIPELINE EXAM PREP
2026 TESTED QUESTIONS AND ANSWERS
GRADED A+
◉ What is the KDD pipeline? Answer: data selection, pre-processing,
transformation, data mining, interpretation/evaluation
◉ Dimensionalities of Data Mining. Answer: -data to be mined
- knowledge to be mined (data mining functions)
- techniques utilized
- applications adapted
◉ What is a data sample? Answer: A subset of data taken from a larger
dataset
◉ What is a dataset? Answer: A collection of related data points or
instances representing all available data.
◉ Different categories of attributes (Categorical). Answer: Nominal-
names of things, categories, states
Binary- nominal attribute with only 2 states (0,1)
Ordinal- values have meaningful order (ranking)
, ◉ Different categories of attributes (Numeric). Answer: Interval-
measured on a scale of equal sized units
Ratio- values are in order of magnitude (
◉ Statistical description of data. Answer: Motivation: tendencies,
variation, spread
Data dispersion: medium, max, min, quantile, outliers, variances
◉ Data Transformation Methods. Answer: - scaling
- logarithmic transformation
- aggregation
- encoding
- binning
- dimensionality reduction
◉ What is EDA? (Exploratory Data Analysis). Answer: An approach in
data analysis to gain insights in understanding of the data, before formal
modeling or hypothesis testing
◉ Motivation of EDA. Answer: To explore and summarize the main
characteristics, patterns, and relationships within the data
2026 TESTED QUESTIONS AND ANSWERS
GRADED A+
◉ What is the KDD pipeline? Answer: data selection, pre-processing,
transformation, data mining, interpretation/evaluation
◉ Dimensionalities of Data Mining. Answer: -data to be mined
- knowledge to be mined (data mining functions)
- techniques utilized
- applications adapted
◉ What is a data sample? Answer: A subset of data taken from a larger
dataset
◉ What is a dataset? Answer: A collection of related data points or
instances representing all available data.
◉ Different categories of attributes (Categorical). Answer: Nominal-
names of things, categories, states
Binary- nominal attribute with only 2 states (0,1)
Ordinal- values have meaningful order (ranking)
, ◉ Different categories of attributes (Numeric). Answer: Interval-
measured on a scale of equal sized units
Ratio- values are in order of magnitude (
◉ Statistical description of data. Answer: Motivation: tendencies,
variation, spread
Data dispersion: medium, max, min, quantile, outliers, variances
◉ Data Transformation Methods. Answer: - scaling
- logarithmic transformation
- aggregation
- encoding
- binning
- dimensionality reduction
◉ What is EDA? (Exploratory Data Analysis). Answer: An approach in
data analysis to gain insights in understanding of the data, before formal
modeling or hypothesis testing
◉ Motivation of EDA. Answer: To explore and summarize the main
characteristics, patterns, and relationships within the data
