AIIM Certified Integrated Risk Management
Consultant CIRMC Exam
**Question 1.** Which probability distribution is most appropriate for modeling the number of
claim events occurring in a fixed time interval when events are rare and independent?
A) Normal
B) Lognormal
C) Poisson
D) Uniform
Answer: C
Explanation: The Poisson distribution models the count of rare, independent events in a fixed
interval, making it ideal for claim frequencies.
**Question 2.** In a risk‑return analysis, the standard deviation of portfolio losses is used to
measure:
A) Expected loss
B) Systematic risk only
C) Total risk (volatility)
D) Skewness of loss distribution
Answer: C
Explanation: Standard deviation quantifies overall variability (volatility) of portfolio outcomes,
reflecting total risk.
**Question 3.** Which of the following best describes Value at Risk (VaR) at the 95%
confidence level for a portfolio?
A) The average loss exceeding the 95th percentile
B) The maximum possible loss
C) The loss that will not be exceeded 95% of the time
D) The probability of a loss larger than the mean
Answer: C
, AIIM Certified Integrated Risk Management
Consultant CIRMC Exam
Explanation: VaR indicates the loss threshold that will not be exceeded with a specified
confidence (95% here).
**Question 4.** Expected Shortfall (ES) improves on VaR because it:
A) Uses a lower confidence level
B) Provides the average loss beyond the VaR threshold
C) Ignores tail risk
D) Is always smaller than VaR
Answer: B
Explanation: ES (or CVaR) calculates the mean of losses that exceed VaR, capturing tail risk more
comprehensively.
**Question 5.** In Monte Carlo simulation, the purpose of generating thousands of random
scenarios is to:
A) Eliminate the need for historical data
B) Approximate the probability distribution of outcomes
C) Reduce computational time
D) Guarantee the optimal solution
Answer: B
Explanation: Simulating many random paths creates an empirical distribution of possible
outcomes, enabling risk estimation.
**Question 6.** A time‑series model that incorporates past values of the variable and past
forecast errors is known as:
A) ARIMA
B) GARCH
C) Monte Carlo
, AIIM Certified Integrated Risk Management
Consultant CIRMC Exam
D) Logistic regression
Answer: A
Explanation: ARIMA (AutoRegressive Integrated Moving Average) combines autoregression,
differencing, and moving‑average components.
**Question 7.** Data governance that ensures “dark data” is identified and either integrated or
eliminated primarily addresses:
A) Data privacy
B) Data quality
C) Data lineage
D) Data encryption
Answer: B
Explanation: Identifying dark (unused or hidden) data improves overall data quality and
relevance for risk analysis.
**Question 8.** In stress testing, a “sensitivity analysis” examines:
A) The probability of extreme events
B) How changes in a single input affect output
C) The correlation among risk factors
D) The historical frequency of losses
Answer: B
Explanation: Sensitivity analysis isolates one variable to assess its impact on model results,
useful for scenario planning.
**Question 9.** The Delphi technique is most useful for:
A) Quantifying loss distributions
, AIIM Certified Integrated Risk Management
Consultant CIRMC Exam
B) Generating consensus among experts on uncertain risks
C) Conducting root‑cause analysis
D) Automating data collection
Answer: B
Explanation: Delphi uses iterative questionnaires with anonymous experts to converge on
informed judgments.
**Question 10.** Which cognitive bias leads risk assessors to give disproportionate weight to
the first piece of information they receive?
A) Confirmation bias
B) Anchoring bias
C) Overconfidence bias
D) Availability bias
Answer: B
Explanation: Anchoring bias causes reliance on initial data, potentially skewing subsequent risk
evaluations.
**Question 11.** A “Black Swan” event is characterized by all of the following EXCEPT:
A) Extreme impact
B) Predictability based on past data
C) Retrospective rationalization
D) Low prior probability
Answer: B
Explanation: Black Swans are inherently unpredictable; they cannot be forecast using historical
data.
Consultant CIRMC Exam
**Question 1.** Which probability distribution is most appropriate for modeling the number of
claim events occurring in a fixed time interval when events are rare and independent?
A) Normal
B) Lognormal
C) Poisson
D) Uniform
Answer: C
Explanation: The Poisson distribution models the count of rare, independent events in a fixed
interval, making it ideal for claim frequencies.
**Question 2.** In a risk‑return analysis, the standard deviation of portfolio losses is used to
measure:
A) Expected loss
B) Systematic risk only
C) Total risk (volatility)
D) Skewness of loss distribution
Answer: C
Explanation: Standard deviation quantifies overall variability (volatility) of portfolio outcomes,
reflecting total risk.
**Question 3.** Which of the following best describes Value at Risk (VaR) at the 95%
confidence level for a portfolio?
A) The average loss exceeding the 95th percentile
B) The maximum possible loss
C) The loss that will not be exceeded 95% of the time
D) The probability of a loss larger than the mean
Answer: C
, AIIM Certified Integrated Risk Management
Consultant CIRMC Exam
Explanation: VaR indicates the loss threshold that will not be exceeded with a specified
confidence (95% here).
**Question 4.** Expected Shortfall (ES) improves on VaR because it:
A) Uses a lower confidence level
B) Provides the average loss beyond the VaR threshold
C) Ignores tail risk
D) Is always smaller than VaR
Answer: B
Explanation: ES (or CVaR) calculates the mean of losses that exceed VaR, capturing tail risk more
comprehensively.
**Question 5.** In Monte Carlo simulation, the purpose of generating thousands of random
scenarios is to:
A) Eliminate the need for historical data
B) Approximate the probability distribution of outcomes
C) Reduce computational time
D) Guarantee the optimal solution
Answer: B
Explanation: Simulating many random paths creates an empirical distribution of possible
outcomes, enabling risk estimation.
**Question 6.** A time‑series model that incorporates past values of the variable and past
forecast errors is known as:
A) ARIMA
B) GARCH
C) Monte Carlo
, AIIM Certified Integrated Risk Management
Consultant CIRMC Exam
D) Logistic regression
Answer: A
Explanation: ARIMA (AutoRegressive Integrated Moving Average) combines autoregression,
differencing, and moving‑average components.
**Question 7.** Data governance that ensures “dark data” is identified and either integrated or
eliminated primarily addresses:
A) Data privacy
B) Data quality
C) Data lineage
D) Data encryption
Answer: B
Explanation: Identifying dark (unused or hidden) data improves overall data quality and
relevance for risk analysis.
**Question 8.** In stress testing, a “sensitivity analysis” examines:
A) The probability of extreme events
B) How changes in a single input affect output
C) The correlation among risk factors
D) The historical frequency of losses
Answer: B
Explanation: Sensitivity analysis isolates one variable to assess its impact on model results,
useful for scenario planning.
**Question 9.** The Delphi technique is most useful for:
A) Quantifying loss distributions
, AIIM Certified Integrated Risk Management
Consultant CIRMC Exam
B) Generating consensus among experts on uncertain risks
C) Conducting root‑cause analysis
D) Automating data collection
Answer: B
Explanation: Delphi uses iterative questionnaires with anonymous experts to converge on
informed judgments.
**Question 10.** Which cognitive bias leads risk assessors to give disproportionate weight to
the first piece of information they receive?
A) Confirmation bias
B) Anchoring bias
C) Overconfidence bias
D) Availability bias
Answer: B
Explanation: Anchoring bias causes reliance on initial data, potentially skewing subsequent risk
evaluations.
**Question 11.** A “Black Swan” event is characterized by all of the following EXCEPT:
A) Extreme impact
B) Predictability based on past data
C) Retrospective rationalization
D) Low prior probability
Answer: B
Explanation: Black Swans are inherently unpredictable; they cannot be forecast using historical
data.
