AIDA 181 - Big Data Analytics for Risk and Insurance Exam Prep| Questions and Verified Answers| 100% Correct (Latest 2023/ 2024 Update)

AIDA 181 - Big Data Analytics for Risk and

Insurance Exam Prep| Questions and

Verified Answers| 100% Correct (Latest

2023/ 2024 Update)

Q: machine learning

Answer:

AI in which computers continually teach themselves to make better decisions on previous results

and new data

Q: Artificial intelligence

Answer:

compute processing/output that simulates reasoning or knowledge

Q: why do tech companies use AI

Answer:

Tech companies use this to "Solve problems or to introduce new products"

Q: data science

Answer:

An interdisciplinary field involving the design and use of techniques to process very large

amounts of data from a variety of sources and to provide knowledge based on the data

Q: structured data

example

Answer:

data organized into databases with defined fields, including links between databases

Microsoft Access

Q: unstructured data

Answer:

Data that is not organized into predetermined formats. Such as databases, and often consists of

texts, images, or other nontraditional media

Q: internal data

examples

Answer:

data that is owned by an organization

premium records, adjuster photos

Q: external data

Answer:

Data that belongs to an entity other than the organization that wishes to acquire and use it

Q: describe economic data in terms of external data

Answer:

Data regarding interest rates, asset prices, exchange rates, the Consumer Price Index, and other

information about the global, the national or a regional economy

Q: geodemographic data

Answer:

data regarding classifications of a population

Q: what is the motto for data quality

Answer:

garbage in - garbage out

Q: list some examples of typical data quality problems

Answer:

missing data / NULL values

duplicate records

default values rather then actual values

Q: ways to detect data quality problems

Answer:

descriptive statistics

data cubes

Q: descriptive statistics

Answer:

Qualitative summaries of the characteristics of a data set, such as the total or average

Q: data cubes

Answer:

A multidimensional partitioning of data into two or more categories

Q: metadata

Answer:

data about the data that provides context for analyzing transaction facts with efficient structures

for grouping hierarchical information

Q: what type of data helps prevent data quality issues?

Answer:

metadata

Q: what are some data mining techniques

Answer:

classification

regression analysis

association rule learning

cluster analysis