Chapter 4 MCQs

Question 1

What is the main benefit of data categorization for operational risk management?
a) Enabling regulatory reporting
b) Facilitating internal control
c) Allowing data aggregation and analysis
d) Ensuring data quality

Answer 1

C

Question 2

Which is NOT usually included as an element in an operational risk data categorization scheme?
a) Risk types
b) Process workflows
c) Geopolitical environments
d) Control types

Answer 2

C

Question 3

What is the main distinction between taxonomy and categorization in operational risk data schemes?
a) Taxonomy only applies to risk events, categorization applies to other data
b) Taxonomy is mandated, categorization is discretionary
c) Taxonomy is qualitative, categorization is quantitative
d) There is no difference

Answer 3

A

Question 4

Scenario analysis in operational risk relies on which data categorization scheme element?
a) Loss event types
b) Key risk indicators
c) Causal factors
d) Business environments

Answer 4

C

Question 5

Why can definitions pose a challenge in developing a data categorization scheme?
a) They make the scheme too complex
b) They may not be universally understood
c) They have to be approved by regulators
d) They are unimportant

Answer 5

B

Question 6

Which is NOT a typical operational risk data categorization element?
a) Strategic objectives
b) Distribution channels
c) Products and services
d) Geographies

Answer 6

A

Question 7

What term refers to recurring high-level business activities?
a) Process workflows
b) Process types
c) Process instances
d) Process elements

Answer 7

B

Question 8

Which Basel II operational risk event type is most distinct?
a) Internal fraud
b) External fraud
c) Business disruption
d) Damage to physical assets

Answer 8

D

Question 9

When categorizing credit cards for operational risk analysis, which attribute would typically be the LOWEST level distinguisher?
a) Network (Visa, Mastercard)
b) Card material (plastic, metal)
c) Rewards program (miles, cashback)
d) Card type (premium, standard)

Answer 9

C

Question 10

Which statement about operational risk data granularity is TRUE?
a) Higher is always better for accuracy
b) Finding the optimal level is challenging
c) External benchmarks determine level
d) Should be minimized to reduce cost

Answer 10

B

Question 11

What is the MAIN reason firms customize industry standard categorization schemes?
a) To enable regulatory compliance
b) To tailor output for internal reporting
c) To improve data quality
d) To facilitate external benchmarking

Answer 11

B

Question 12

In the bow-tie model, what element occurs between cause and impact?
a) Threat source
b) Risk agent
c) Adverse event
d) Loss driver

Answer 12

C

Question 13

Which LEAST influences the choice of categorization scheme elements?
a) Existing data repositories
b) Management preferences
c) Resource availability
d) Business structure and objectives

Answer 13

C

Question 14

RCSAs commonly use which data categorization elements?
a) Process and product only
b) Industry, geography and client only
c) Risk, control and business line only
d) Risk, process and control type

Answer 14

D

Question 15

Why should definitions used in an operational risk taxonomy relate to business terminology?
a) To enable automated categorization
b) To facilitate regulatory approval
c) To promote adoption by managers
d) To enhance audit standards

Answer 15

C

Question 16

Which presents the GREATEST data quality challenge in operational risk categorisation?
a) Lack of historical data
b) Complex category structures
c) Inconsistent human decision making
d) Category duplication

Answer 16

C

Question 17

What is the MAIN disadvantage of a highly granular categorization scheme?
a) Harder to gain management approval
b) Insufficient for advanced modeling
c) Unable to handle new data sources
d) Vulnerable to misinterpretation

Answer 17

A

Question 18

Which operational risk tool would NOT directly rely on risk categorization?
a) Audit sampling
b) Capital modeling
c) Risk appetite definition
d) Control testing

Answer 18

C

Question 19

Scenario analysis utilizes which data scheme elements?
a) Loss events, key risk indicators
b) Risk registers, loss events
c) Audit findings, risk incidents
d) Possible causes, imagined impacts

Answer 19

D

Question 20

What issue can reduce adoption of a risk taxonomy across an organization?
a) Staff training costs
b) Required systems upgrades
c) Inconsistent terminology
d) Lack of regulatory mandate

Answer 20

C

Question 21

Why should operational risk managers understand organizational biases?
a) To counteract incorrect risk models
b) To challenge unrealistic assumptions
c) To prevent unfair staff evaluations
d) To build consensus on risk viewpoint

Answer 21

B

Question 22

Which is the BEST location for initial operational risk data categorization?
a) Data entry staff near business functions
b) Data analytics team in central office
c) Risk and research department
d) Management steering committee

Answer 22

A

Question 23

What is the MAIN motivation for external risk data benchmarking?
a) Comparing insurance costs
b) Improving regulatory standing
c) Assessing performance versus peer organizations
d) Promoting advanced risk quantificatio

Answer 23

C

Question 24

Big data and machine learning enable automated real-time risk data categorization. What issue may still require expert review?
a) Incident causation logic
b) Impact calculations
c) Risk taxonomy refresh
d) Outlier detection

Answer 24

A

Question 25

Effective key risk indicator design uses multidimensional data categorization schemes to enable?
a) Actionable risk insights
b) Data mining and discovery
c) Real time alerts
d) Artificial intelligence integration

Answer 25

A

Question 26

Why should operational risk taxonomy include appropriate regional or geographic distinctions?
a) Enables centralized data collection
b) Supports regional autonomy over standards
c) Accounts for regulatory and business variations
d) Prevents data sharing across borders

Answer 26

C

Question 27

What risk taxonomy challenge requires balancing comprehensiveness and usability?
a) Data storage capacity
b) Hierarchical levels and complexity
c) Descriptive terminology and naming conventions
d) Required frequency of comprehensive review

Answer 27

B

Question 28

Who should have decision rights over choice of data categorization scheme standards?
a) Chief Data Officer
b) Chief Risk Officer
c) Board Risk Committee
d) External industry association

Answer 28

B

Question 29

Effective root cause analysis in operational risk management requires?
a) Causal data taxonomy
b) Bow tie methodology
c) Near miss data only
d) Evidence based assessment

Answer 29

B

Question 30

Why do operational risk managers prefer composite indicators over unstructured big data for risk monitoring?
a) Intuitive graphical representation
b) Holistic insights
c) Automated risk thresholds
d) Streamlined data collection

Answer 30

B