BIG DATA ANALYTICS FOR RISK AND
INSURANCE CERTIFICATION EVALUATION 2026
TEST QUESTIONS AND ANSWERS
◉ Data science. Answer: interdisciplinary field involving design &
use of techniques to process very large amounts of data from a
variety of sources, and to provide knowledge based on that data
◉ Data-driven decision making. Answer: the organizational process
to gather and analyze relevant and verifiable data and then evaluate
the results to guide business strategies
◉ Data literacy. Answer: the ability to collect, read, communicate
with, question, and apply data in ways that enhance business
decisions/results
◉ 3 approaches to data-driven decision making. Answer: Descriptive
analytics
Predictive analytics
Prescriptive analytics
,◉ Descriptive analytics. Answer: looking at data to determine what
has occurred
◉ Predictive analytics. Answer: looking at data to predict what may
happen in the future
◉ Prescriptive analytics. Answer: Looking at data to decide what to
do in the future
◉ Data engineering. Answer: Creating the processes and
infrastructure needed to collect, store, and analyze data
◉ Data exploration. Answer: An initial analysis of data to determine
what's in a dataset, including obvious patterns and anomalies
◉ Data cleaning. Answer: Correcting quality issues in the data, such
as errors, inaccuracies, and duplicate entries
◉ Data wrangling. Answer: Organizing data for analysis
◉ Data manipulation. Answer: Transforming data for analysis
, ◉ Data mining. Answer: Analyzing large amounts of data to find new
relationships and patterns that will assist in data-driven decision
making
◉ Data modeling. Answer: The visual representation of mined data,
which is used to further define and analyze data to determine how it
relates to other data
◉ Data visualization. Answer: The representation of data through
visuals such as graphs, charts, plots, images, infographics,
animations, and photos
◉ Data storytelling. Answer: Communicating a narrative using
insights from a dataset to influence decision making
◉ Supervised learning. Answer: Data analysis/learning technique in
which the target variable (item being predicted) is defined; datasets
labeled
◉ Unsupervised learning. Answer: Data analysis/learning technique
in which model does not have a defined target variable; datasets not
labeled.
◉ Predictive model. Answer: Model used to estimate a target value;
can be used to determine unknown values in past or present
INSURANCE CERTIFICATION EVALUATION 2026
TEST QUESTIONS AND ANSWERS
◉ Data science. Answer: interdisciplinary field involving design &
use of techniques to process very large amounts of data from a
variety of sources, and to provide knowledge based on that data
◉ Data-driven decision making. Answer: the organizational process
to gather and analyze relevant and verifiable data and then evaluate
the results to guide business strategies
◉ Data literacy. Answer: the ability to collect, read, communicate
with, question, and apply data in ways that enhance business
decisions/results
◉ 3 approaches to data-driven decision making. Answer: Descriptive
analytics
Predictive analytics
Prescriptive analytics
,◉ Descriptive analytics. Answer: looking at data to determine what
has occurred
◉ Predictive analytics. Answer: looking at data to predict what may
happen in the future
◉ Prescriptive analytics. Answer: Looking at data to decide what to
do in the future
◉ Data engineering. Answer: Creating the processes and
infrastructure needed to collect, store, and analyze data
◉ Data exploration. Answer: An initial analysis of data to determine
what's in a dataset, including obvious patterns and anomalies
◉ Data cleaning. Answer: Correcting quality issues in the data, such
as errors, inaccuracies, and duplicate entries
◉ Data wrangling. Answer: Organizing data for analysis
◉ Data manipulation. Answer: Transforming data for analysis
, ◉ Data mining. Answer: Analyzing large amounts of data to find new
relationships and patterns that will assist in data-driven decision
making
◉ Data modeling. Answer: The visual representation of mined data,
which is used to further define and analyze data to determine how it
relates to other data
◉ Data visualization. Answer: The representation of data through
visuals such as graphs, charts, plots, images, infographics,
animations, and photos
◉ Data storytelling. Answer: Communicating a narrative using
insights from a dataset to influence decision making
◉ Supervised learning. Answer: Data analysis/learning technique in
which the target variable (item being predicted) is defined; datasets
labeled
◉ Unsupervised learning. Answer: Data analysis/learning technique
in which model does not have a defined target variable; datasets not
labeled.
◉ Predictive model. Answer: Model used to estimate a target value;
can be used to determine unknown values in past or present
