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AIDA 181 - SIMULATED EXAM QUESTIONS AND ANSWERS

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AIDA 181 - SIMULATED EXAM QUESTIONS AND ANSWERS

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AIDA 181 - SIMULATED EXAM
QUESTIONS AND ANSWERS

Which one of the following pairs represents internal environmental factors that could be
affected by a data analytics initiative? -A. Opportunities and strengths
B. Strengths and weaknesses
C. Threats and opportunities
D. Weaknesses and threats

** CORRECT: Strengths and weaknesses

If clusters are grouped at one level of the hierarchy, -A. They are only grouped at that
one level of the hierarchy.
B. They are grouped at all levels of the hierarchy.
C. They are grouped at each lower level of the hierarchy.
D. They are grouped at each higher level of the hierarchy.

** CORRECT: They are grouped at each higher level of the hierarchy.

Which one of the following data governance tools allows the data governance
committee to look at data relationships and interdependencies across the organization?
-A. Internal coding procedures
B. Enterprise data models
C. Project management programs
D. External compliance guidelines

** CORRECT: Enterprise data models

A neural network can work with -A. Categorical values only.
B. Numerical values only.
C. Numerical and categorical values.
D. Probability values only.

** CORRECT: Numerical values only.

Encrypting data is an example of -A. A data governance program.
B. A data security program.
C. A regulatory compliance program.
D. An enterprise risk management program

** CORRECT: A data security program.

, Greatview Insurance is a regional insurer. The profitability of its homeowners book has
been declining for several years. In the past, Greatview has been able to maintain
profitability by using traditional rating variables, and implementing flat rate increases
across the entire book when necessary. Management believes that is the only way to
improve profitability. Which one of the following would most likely occur if Greatview
continues to apply flat rate increases across the homeowners book? -A. The customers
with the lower loss ratios would most likely find coverage elsewhere, and the overall
retention and loss ratio would increase.
B. The customers with higher loss ratios would be more likely to stay, leaving the
insurer with the least profitable segment of its book. The overall loss ratio would most
likely not improve.
C. The customers with higher loss ratios would most likely find coverage elsewhere, and
the overall retention and loss ratio would decrease.
D. The customers with lower loss ratios would most likely pay the increase and stay,
and the customers with the higher loss ratio would leave, resulting in an improved loss
ratio for the book.

** CORRECT: The customers with higher loss ratios would be more likely to stay,
leaving the insurer with the least profitable segment of its book. The overall loss ratio
would most likely not improve.

Usage-based insurance (UBI) is -A. A type of auto insurance in which the premium is
based on the policyholder's driving behavior.
B. Based on attributes such as driving record, driver education, years of driving
experience, and credit score.
C. A device that tracks driving habits regarding braking, acceleration, speed, cornering,
lane shifting, left turns versus right turns, and the time of day the vehicle is driven.
D. A risk-management technique used for fleet safety programs to monitor employees'
driving behaviors but not to price insurance.

** CORRECT: A type of auto insurance in which the premium is based on the
policyholder's driving behavior.

A predictive model is applied to a clothing manufacturer's data of 1,000 employees, 50
of whom had workplace injuries in the past year. The table below shows how often the
model correctly and incorrectly predicts for each employee "yes, will have an accident"
or "no, will not have an accident."
Predicted No+Predicted Yes=Total (1,000 Employees)
Actual No 945 5 950
Actual Yes 10 40 50
Based on the preceding number, these statements can be made:
There are 40 true positives (TP) for which the model correctly predicted yes.
There are 945 true negatives (TN) for which the model correctly predicted no.
There are 5 false positives (FP) for which the model incorrectly predicted yes (and the
actual answer is no).

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