DTSA 5001 ACTUAL EXAM PAPER 2026
COMPLETE QUESTIONS AND CORRECT
ANSWERS
◉ Anomaly Detection
Answer: Includes anomalies or outliers (e.g. error, noise, fraud,
extreme events)
◉ Trend and Evolution Analysis
Answer: Includes changes over time, overall trend, periodical
patterns, anomalies (e.g. Google Trends)
◉ What Steps Form The Data Mining Pipeline?
Answer: Data Understanding, Data Preprocessing, Data
Warehousing, Data Modeling, Pattern Evaluation
◉ Data Understanding
Answer: Answering questions like: What types of data? What do
they look like?
Includes statistics and visualization
,observes similarity vs dissimilarity
◉ data preprocessing
Answer: Preparing the data for the mining process, includes the
following operations: Data Integration, Data Transformation, Data
Reduction, Data Cleaning
◉ What Potential Issues Are There With Data?
Answer: Missing data, errors, inconsistency
◉ Data Warehousing
Answer: the collection, storage, and retrieval of data in electronic
files. Includes operational data. Can involve a data cube and OLAP
◉ Data Modeling
Answer: Step that involves the five technique views of data mining
◉ Pattern Evaluation
Answer: Involves finding interesting patterns from data, use of
evaluation metrics and model selection
◉ What are some examples of data mining?
, Answer: Business intelligence, cyberspace, healthcare,
transportation electrification, group event scheduling, remote
sensing
◉ What are some major issues in data mining?
Answer: Diverse data requires diverse knowledge, data quality
issues, supervised vs unsupervised learning, performance
evaluation, effectiveness vs efficiency, incremental and interactive
mining, integration of domain knowledge, visual analytics, privacy-
preserving mining
◉ What are the data ethics related to data mining?
Answer: Data ownership, privacy/anonymity, data and model
validity, data and model bias (algorithmic fairness), interpretation,
application, societal consequence
◉ What aspects are part of the stage of data understanding?
Answer: Data objects and attributes, data statistics, data
visualization, data similarity
◉ What is a dataset?
Answer: A collection of data objects, where each object is described
by a number of attributes
COMPLETE QUESTIONS AND CORRECT
ANSWERS
◉ Anomaly Detection
Answer: Includes anomalies or outliers (e.g. error, noise, fraud,
extreme events)
◉ Trend and Evolution Analysis
Answer: Includes changes over time, overall trend, periodical
patterns, anomalies (e.g. Google Trends)
◉ What Steps Form The Data Mining Pipeline?
Answer: Data Understanding, Data Preprocessing, Data
Warehousing, Data Modeling, Pattern Evaluation
◉ Data Understanding
Answer: Answering questions like: What types of data? What do
they look like?
Includes statistics and visualization
,observes similarity vs dissimilarity
◉ data preprocessing
Answer: Preparing the data for the mining process, includes the
following operations: Data Integration, Data Transformation, Data
Reduction, Data Cleaning
◉ What Potential Issues Are There With Data?
Answer: Missing data, errors, inconsistency
◉ Data Warehousing
Answer: the collection, storage, and retrieval of data in electronic
files. Includes operational data. Can involve a data cube and OLAP
◉ Data Modeling
Answer: Step that involves the five technique views of data mining
◉ Pattern Evaluation
Answer: Involves finding interesting patterns from data, use of
evaluation metrics and model selection
◉ What are some examples of data mining?
, Answer: Business intelligence, cyberspace, healthcare,
transportation electrification, group event scheduling, remote
sensing
◉ What are some major issues in data mining?
Answer: Diverse data requires diverse knowledge, data quality
issues, supervised vs unsupervised learning, performance
evaluation, effectiveness vs efficiency, incremental and interactive
mining, integration of domain knowledge, visual analytics, privacy-
preserving mining
◉ What are the data ethics related to data mining?
Answer: Data ownership, privacy/anonymity, data and model
validity, data and model bias (algorithmic fairness), interpretation,
application, societal consequence
◉ What aspects are part of the stage of data understanding?
Answer: Data objects and attributes, data statistics, data
visualization, data similarity
◉ What is a dataset?
Answer: A collection of data objects, where each object is described
by a number of attributes
