BIG DATA ANALYTICS FOR RISK AND
INSURANCE ACTUAL SCRIPT 2026 QUESTIONS
AND ANSWERS 100% CORRECT
◉ Cluster analysis Answer: Is an unsupervised learning technique
◉ In a data analytical model, the predefined attribute whose value is
being predicted is known as the Answer: Target variable.
◉ Which one of the following is an example of exploratory data
analysis? Answer: Correlation matrix
◉ Which one of the following statements regarding training data for
a classification tree is correct? Answer: Training data has a known
value for the target variable.
◉ The two types of descriptions for clusters are Answer:
Characteristic and differential.
◉ In a k-means clustering algorithm, Answer: "k" indicates the
number of clusters, and "means" represents the clusters' centroids
,◉ Johanna is claim manager for Goshen Mutual. She is working with
the data science team to develop a way to determine the
characteristics of slip and fall on premises claims that end up going
to litigation. Johanna would like to use this information to help her
assign new claims to her staff. The data science team is applying the
k nearest neighbor technique to the data on slip and fall claims that
have gone to litigation. The model reveals that 6 of a new claim's
nearest neighbors in the dataset did go to litigation and 2 of the
nearest neighbors did not go to litigation. Using the voting method,
which one of the following can Johanna predict about the new claim?
Answer: The new claim is likely to go to litigation
◉ Which one of the following is a simple data analysis technique
that could be used to show the relationship between two attributes?
Answer: Scatter plot
◉ Which one of the following traditional data analysis techniques
would be used when an insurer wants to determine which
characteristics lead to an increase in the severity of workers
compensation claims but does not know which variables it must
analyze to do so? Answer: Cluster analysis
◉ In a k nearest neighbor clustering algorithm, Answer: The higher
the number of k, the more accurate the prediction is likely to be.
, ◉ Tree-based probabilities are calculated by Answer: Dividing the
number of times the model correctly predicted the value by the total
predictions at each leaf node.
◉ If an insurer wants to determine the numerical value for a known
target variable, it is most likely to use Answer: Regression.
◉ Grant Insurance is working with its data scientists who
recommend cluster analysis for the project that the risk managers
have described. Which one of the following is true regarding this?
Answer: Several iterations of cluster analysis can be applied to
provide more granular information.
◉ An online retailer uses data on products that a customer has
purchased to recommend additional products to the customer.
Which one of the following data analysis techniques is the retailer
using? Answer: Association rule learning
◉ In text mining, a collection of documents is called a(n) Answer:
Corpus.
◉ Which one of the following is the first step in the text mining
process? Answer: Retrieve and prepare text with preprocessing
techniques
INSURANCE ACTUAL SCRIPT 2026 QUESTIONS
AND ANSWERS 100% CORRECT
◉ Cluster analysis Answer: Is an unsupervised learning technique
◉ In a data analytical model, the predefined attribute whose value is
being predicted is known as the Answer: Target variable.
◉ Which one of the following is an example of exploratory data
analysis? Answer: Correlation matrix
◉ Which one of the following statements regarding training data for
a classification tree is correct? Answer: Training data has a known
value for the target variable.
◉ The two types of descriptions for clusters are Answer:
Characteristic and differential.
◉ In a k-means clustering algorithm, Answer: "k" indicates the
number of clusters, and "means" represents the clusters' centroids
,◉ Johanna is claim manager for Goshen Mutual. She is working with
the data science team to develop a way to determine the
characteristics of slip and fall on premises claims that end up going
to litigation. Johanna would like to use this information to help her
assign new claims to her staff. The data science team is applying the
k nearest neighbor technique to the data on slip and fall claims that
have gone to litigation. The model reveals that 6 of a new claim's
nearest neighbors in the dataset did go to litigation and 2 of the
nearest neighbors did not go to litigation. Using the voting method,
which one of the following can Johanna predict about the new claim?
Answer: The new claim is likely to go to litigation
◉ Which one of the following is a simple data analysis technique
that could be used to show the relationship between two attributes?
Answer: Scatter plot
◉ Which one of the following traditional data analysis techniques
would be used when an insurer wants to determine which
characteristics lead to an increase in the severity of workers
compensation claims but does not know which variables it must
analyze to do so? Answer: Cluster analysis
◉ In a k nearest neighbor clustering algorithm, Answer: The higher
the number of k, the more accurate the prediction is likely to be.
, ◉ Tree-based probabilities are calculated by Answer: Dividing the
number of times the model correctly predicted the value by the total
predictions at each leaf node.
◉ If an insurer wants to determine the numerical value for a known
target variable, it is most likely to use Answer: Regression.
◉ Grant Insurance is working with its data scientists who
recommend cluster analysis for the project that the risk managers
have described. Which one of the following is true regarding this?
Answer: Several iterations of cluster analysis can be applied to
provide more granular information.
◉ An online retailer uses data on products that a customer has
purchased to recommend additional products to the customer.
Which one of the following data analysis techniques is the retailer
using? Answer: Association rule learning
◉ In text mining, a collection of documents is called a(n) Answer:
Corpus.
◉ Which one of the following is the first step in the text mining
process? Answer: Retrieve and prepare text with preprocessing
techniques
