Associate in Insurance Data Analytics AIDA
Practice Exam
**Question 1.** Which of the following most accurately describes the shift from traditional
actuarial methods to predictive modeling in insurance?
A) Reliance on deterministic tables only
B) Use of static risk factors without machine learning
C) Integration of large‑scale data and algorithms to forecast outcomes
D) Elimination of human judgment in pricing
Answer: C
Explanation: Predictive modeling leverages extensive data sets and statistical/machine‑learning
algorithms to produce dynamic forecasts, moving beyond static actuarial tables.
**Question 2.** In the insurance data ecosystem, which of the following is considered external
data?
A) Policy issuance dates
B) Claims adjustment notes
C) Weather radar feeds
D) Billing transaction logs
Answer: C
Explanation: Weather radar feeds originate outside the insurer and are used to enrich risk
assessments, making them external data.
**Question 3.** Structured data differs from unstructured data primarily because it:
A) Contains only text narratives
B) Is stored in fixed fields and relational tables
C) Requires natural language processing to interpret
, Associate in Insurance Data Analytics AIDA
Practice Exam
D) Cannot be queried with SQL
Answer: B
Explanation: Structured data fits into predefined schemas (e.g., rows and columns) and can be
directly queried using SQL.
**Question 4.** Which phase of the analytics lifecycle focuses on translating business
objectives into a data‑driven problem statement?
A) Data Preparation
B) Business Understanding
C) Modeling
D) Deployment
Answer: B
Explanation: Business Understanding defines the problem, scope, and success criteria before
data work begins.
**Question 5.** Descriptive analytics in insurance is primarily used for:
A) Predicting future claim frequency
B. Recommending optimal pricing strategies
C. Summarizing past loss trends
D. Automating underwriting decisions
Answer: C
Explanation: Descriptive analytics summarizes historical data, such as loss trends, without
forecasting or prescribing actions.
, Associate in Insurance Data Analytics AIDA
Practice Exam
**Question 6.** Which of the following best exemplifies a diagnostic analytics use case in an
insurer?
A) Forecasting next quarter’s premium revenue
B) Identifying why loss ratios spiked in a particular region
C) Setting dynamic pricing based on telematics data
D) Automating policy renewals
Answer: B
Explanation: Diagnostic analytics seeks root causes; investigating a regional loss‑ratio spike fits
this purpose.
**Question 7.** A data lake is most suitable for:
A) Storing only cleaned, relational data
B) Holding raw, heterogeneous data at scale
C) Enforcing strict schema on write
D) Replacing all data‑warehouse functionality
Answer: B
Explanation: Data lakes accept raw, varied formats (structured, semi‑structured, unstructured)
without enforcing schemas upfront.
**Question 8.** Which data‑quality dimension measures whether a data element is recorded at
the correct point in time?
A) Accuracy
B) Timeliness
, Associate in Insurance Data Analytics AIDA
Practice Exam
C) Completeness
D) Consistency
Answer: B
Explanation: Timeliness assesses how current or up‑to‑date the data is relative to its intended
use.
**Question 9.** Master Data Management (MDM) primarily aims to:
A) Archive historical transaction logs
B) Create a single source of truth for core entities
C) Increase data redundancy across systems
D) Automate claim adjudication
Answer: B
Explanation: MDM consolidates and governs master entities (customers, products) to ensure
consistency across the organization.
**Question 10.** In insurance, the Poisson distribution is most appropriate for modeling:
A) Claim severity amounts
B) Number of claims per policy period
C) Policyholder age
D) Premium income variance
Answer: B
Explanation: The Poisson distribution models count data, such as the frequency of claims
occurring in a fixed interval.
Practice Exam
**Question 1.** Which of the following most accurately describes the shift from traditional
actuarial methods to predictive modeling in insurance?
A) Reliance on deterministic tables only
B) Use of static risk factors without machine learning
C) Integration of large‑scale data and algorithms to forecast outcomes
D) Elimination of human judgment in pricing
Answer: C
Explanation: Predictive modeling leverages extensive data sets and statistical/machine‑learning
algorithms to produce dynamic forecasts, moving beyond static actuarial tables.
**Question 2.** In the insurance data ecosystem, which of the following is considered external
data?
A) Policy issuance dates
B) Claims adjustment notes
C) Weather radar feeds
D) Billing transaction logs
Answer: C
Explanation: Weather radar feeds originate outside the insurer and are used to enrich risk
assessments, making them external data.
**Question 3.** Structured data differs from unstructured data primarily because it:
A) Contains only text narratives
B) Is stored in fixed fields and relational tables
C) Requires natural language processing to interpret
, Associate in Insurance Data Analytics AIDA
Practice Exam
D) Cannot be queried with SQL
Answer: B
Explanation: Structured data fits into predefined schemas (e.g., rows and columns) and can be
directly queried using SQL.
**Question 4.** Which phase of the analytics lifecycle focuses on translating business
objectives into a data‑driven problem statement?
A) Data Preparation
B) Business Understanding
C) Modeling
D) Deployment
Answer: B
Explanation: Business Understanding defines the problem, scope, and success criteria before
data work begins.
**Question 5.** Descriptive analytics in insurance is primarily used for:
A) Predicting future claim frequency
B. Recommending optimal pricing strategies
C. Summarizing past loss trends
D. Automating underwriting decisions
Answer: C
Explanation: Descriptive analytics summarizes historical data, such as loss trends, without
forecasting or prescribing actions.
, Associate in Insurance Data Analytics AIDA
Practice Exam
**Question 6.** Which of the following best exemplifies a diagnostic analytics use case in an
insurer?
A) Forecasting next quarter’s premium revenue
B) Identifying why loss ratios spiked in a particular region
C) Setting dynamic pricing based on telematics data
D) Automating policy renewals
Answer: B
Explanation: Diagnostic analytics seeks root causes; investigating a regional loss‑ratio spike fits
this purpose.
**Question 7.** A data lake is most suitable for:
A) Storing only cleaned, relational data
B) Holding raw, heterogeneous data at scale
C) Enforcing strict schema on write
D) Replacing all data‑warehouse functionality
Answer: B
Explanation: Data lakes accept raw, varied formats (structured, semi‑structured, unstructured)
without enforcing schemas upfront.
**Question 8.** Which data‑quality dimension measures whether a data element is recorded at
the correct point in time?
A) Accuracy
B) Timeliness
, Associate in Insurance Data Analytics AIDA
Practice Exam
C) Completeness
D) Consistency
Answer: B
Explanation: Timeliness assesses how current or up‑to‑date the data is relative to its intended
use.
**Question 9.** Master Data Management (MDM) primarily aims to:
A) Archive historical transaction logs
B) Create a single source of truth for core entities
C) Increase data redundancy across systems
D) Automate claim adjudication
Answer: B
Explanation: MDM consolidates and governs master entities (customers, products) to ensure
consistency across the organization.
**Question 10.** In insurance, the Poisson distribution is most appropriate for modeling:
A) Claim severity amounts
B) Number of claims per policy period
C) Policyholder age
D) Premium income variance
Answer: B
Explanation: The Poisson distribution models count data, such as the frequency of claims
occurring in a fixed interval.
