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Mha 705 Modules 5 Exam latest update Practice Questions (Original) EXAM | 2026–2027
Latest Update | Original Questions & Answers with Detailed Rationales | Graded A+
Section 1: Data Mining & Knowledge Discovery
Q1. Data Mining focuses on producing a solution that generates
useful forecasting through a four-phase process. What is the
correct order of these phases?
A. Problem identification, exploration of the data, pattern
discovery, knowledge deployment
B. Exploration of the data, problem identification, knowledge
deployment, pattern discovery
C. Pattern discovery, problem identification, exploration of the
data, knowledge deployment
D. Knowledge deployment, pattern discovery, problem
identification, exploration of the data
Correct Answer: A
Rationale: The four-phase data mining process begins with
problem identification, followed by exploration of the data,
pattern discovery, and finally knowledge deployment. This
structured approach ensures that data mining efforts are goal-
directed and produce actionable insights.
Q2. (T/F) The term "knowledge discovery" is key, as data mining
looks at data from different vantage points, aspects, and
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perspectives and brings new insights to the data set.
A. True
B. False
Correct Answer: A
Rationale: Knowledge discovery is the overarching goal of data
mining. By examining data from multiple perspectives, data
mining uncovers previously hidden patterns and relationships
that can inform decision-making.
Q3. What does KDD stand for in the context of data mining?
A. Knowledge Discovery and Data
B. Knowledge Discovery in Databases
C. Knowledge Data Distribution
D. Knowledge Deployment and Data
Correct Answer: B
Rationale: KDD stands for Knowledge Discovery in Databases.
This term emphasizes that the ultimate purpose of data mining
is to discover meaningful knowledge from large datasets.
Q4. What is data mining?
A. The process of collecting data from multiple sources
B. The process of extracting patterns from data using algorithms
C. The process of storing data in a warehouse
D. The process of deleting redundant data
Correct Answer: B
Rationale: Data mining is concerned with extracting what is
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needed and enumeration of patterns from data using
algorithms. It applies statistics so that organizations can gain an
advantage by manipulating information for practical
applications.
Q5. What is the acronym SEMMA in the context of data mining?
A. Sample, Explore, Modify, Model, Assess
B. Select, Examine, Modify, Monitor, Analyze
C. Sample, Evaluate, Model, Manage, Assess
D. Select, Explore, Model, Modify, Assess
Correct Answer: A
Rationale: SEMMA stands for Sample, Explore, Modify, Model,
and Assess. It refers to the core process of conducting data
mining, developed by the SAS Institute.
Q6. Which data mining model uses the organization's same
business model?
A. CRISP-DM
B. SEMMA
C. Six Sigma
D. DMAIC
Correct Answer: A
Rationale: CRISP-DM (Cross-Industry Standard Process for Data
Mining) is a data mining model that uses the organization's
same business model, ensuring that data mining efforts are
aligned with business objectives.
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Q7. What are the six steps of CRISP-DM?
A. Business understanding, data understanding, data
preparation, modeling, evaluation, deployment
B. Sample, explore, modify, model, assess, deploy
C. Problem identification, data collection, data analysis,
modeling, evaluation, deployment
D. Planning, data collection, data cleansing, modeling,
assessment, implementation
Correct Answer: A
Rationale: The six steps of CRISP-DM are business
understanding, data understanding, data preparation,
modeling, evaluation, and deployment. This methodology
provides a comprehensive framework for data mining projects.
Q8. What is pattern discovery in data mining?
A. A simple phase of data mining
B. A complex phase of data mining
C. A preliminary phase of data mining
D. A final phase of data mining
Correct Answer: B
Rationale: Pattern discovery is a complex phase of data mining.
It involves identifying meaningful patterns and relationships
within large datasets, which requires sophisticated algorithms
and analytical techniques.
Mha 705 Modules 5 Exam latest update Practice Questions (Original) EXAM | 2026–2027
Latest Update | Original Questions & Answers with Detailed Rationales | Graded A+
Section 1: Data Mining & Knowledge Discovery
Q1. Data Mining focuses on producing a solution that generates
useful forecasting through a four-phase process. What is the
correct order of these phases?
A. Problem identification, exploration of the data, pattern
discovery, knowledge deployment
B. Exploration of the data, problem identification, knowledge
deployment, pattern discovery
C. Pattern discovery, problem identification, exploration of the
data, knowledge deployment
D. Knowledge deployment, pattern discovery, problem
identification, exploration of the data
Correct Answer: A
Rationale: The four-phase data mining process begins with
problem identification, followed by exploration of the data,
pattern discovery, and finally knowledge deployment. This
structured approach ensures that data mining efforts are goal-
directed and produce actionable insights.
Q2. (T/F) The term "knowledge discovery" is key, as data mining
looks at data from different vantage points, aspects, and
, Page |2
perspectives and brings new insights to the data set.
A. True
B. False
Correct Answer: A
Rationale: Knowledge discovery is the overarching goal of data
mining. By examining data from multiple perspectives, data
mining uncovers previously hidden patterns and relationships
that can inform decision-making.
Q3. What does KDD stand for in the context of data mining?
A. Knowledge Discovery and Data
B. Knowledge Discovery in Databases
C. Knowledge Data Distribution
D. Knowledge Deployment and Data
Correct Answer: B
Rationale: KDD stands for Knowledge Discovery in Databases.
This term emphasizes that the ultimate purpose of data mining
is to discover meaningful knowledge from large datasets.
Q4. What is data mining?
A. The process of collecting data from multiple sources
B. The process of extracting patterns from data using algorithms
C. The process of storing data in a warehouse
D. The process of deleting redundant data
Correct Answer: B
Rationale: Data mining is concerned with extracting what is
, Page |3
needed and enumeration of patterns from data using
algorithms. It applies statistics so that organizations can gain an
advantage by manipulating information for practical
applications.
Q5. What is the acronym SEMMA in the context of data mining?
A. Sample, Explore, Modify, Model, Assess
B. Select, Examine, Modify, Monitor, Analyze
C. Sample, Evaluate, Model, Manage, Assess
D. Select, Explore, Model, Modify, Assess
Correct Answer: A
Rationale: SEMMA stands for Sample, Explore, Modify, Model,
and Assess. It refers to the core process of conducting data
mining, developed by the SAS Institute.
Q6. Which data mining model uses the organization's same
business model?
A. CRISP-DM
B. SEMMA
C. Six Sigma
D. DMAIC
Correct Answer: A
Rationale: CRISP-DM (Cross-Industry Standard Process for Data
Mining) is a data mining model that uses the organization's
same business model, ensuring that data mining efforts are
aligned with business objectives.
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Q7. What are the six steps of CRISP-DM?
A. Business understanding, data understanding, data
preparation, modeling, evaluation, deployment
B. Sample, explore, modify, model, assess, deploy
C. Problem identification, data collection, data analysis,
modeling, evaluation, deployment
D. Planning, data collection, data cleansing, modeling,
assessment, implementation
Correct Answer: A
Rationale: The six steps of CRISP-DM are business
understanding, data understanding, data preparation,
modeling, evaluation, and deployment. This methodology
provides a comprehensive framework for data mining projects.
Q8. What is pattern discovery in data mining?
A. A simple phase of data mining
B. A complex phase of data mining
C. A preliminary phase of data mining
D. A final phase of data mining
Correct Answer: B
Rationale: Pattern discovery is a complex phase of data mining.
It involves identifying meaningful patterns and relationships
within large datasets, which requires sophisticated algorithms
and analytical techniques.
