GU D491 INTRODUCTION TO
ANALYTICS OA ACTUAL EXAM
2025/2026 COMPLETE
QUESTIONS WITH CORRECT
DETAILED ANSWERS || 100%
GUARANTEED PASS <BRAND
NEW VERSION>
What is Data analytics?
-The process of encrypting data to keep it secure
-The process of storing data in a secure location for future use
-The process of analyzing data to extract insights
-The process of collecting data from various sources - ANSWER The
process of analyzing data to extract insights. (Data analytics involves
analyzing data to extract insights and inform decision-making. This
includes using various techniques and tools to explore, clean, transform,
and model data and visualize and communicate findings.)
Which type of data analytics project aims to determine why something
happened in the past?
-Prescriptive
-Descriptive
-Predictive
-Diagnostic - ANSWER Descriptive (Descriptive analytics focuses on
summarizing past events and understanding what happened.)
What are the different types of data analytics projects?
-Regression analysis, time series analysis, text analytics, and network
analysis
-Data warehousing, data mining, data visualization, and business
intelligence
-Descriptive, diagnostic, predictive, and prescriptive analytics
,-Data collection, data cleaning, data transformation, and data
visualization - ANSWER Descriptive, diagnostic, predictive, and
prescriptive analytics
What is the difference between exploratory and confirmatory data
analytics projects?
-Exploratory projects involve testing hypotheses and finding patterns in
data, while confirmatory projects involve verifying existing hypotheses.
-Exploratory projects involve analyzing data from a single source, while
confirmatory projects involve integrating data from multiple sources.
-Exploratory projects involve analyzing data that is already structured,
while confirmatory projects involve analyzing unstructured data.
-Exploratory projects involve analyzing large datasets, while
confirmatory projects involve analyzing smaller datasets. - ANSWER
Exploratory projects involve testing hypotheses and finding patterns in
data, while confirmatory projects involve verifying existing hypotheses.
(Exploratory data analytics projects are typically used when little is
known about the data or when researchers look for patterns or trends that
may not have been previously identified.)
Which project is considered a data analytics project?
-Developing a recommendation system to suggest new products to
customers based on their past purchases
-Creating a dashboard to visualize sales data and monitor inventory levels
for a grocery store chain
-Building a predictive model to forecast stock prices for a financial
services company
-Designing a database schema to store customer information for a retail
store - ANSWER Creating a dashboard to visualize sales data and
monitor inventory levels for a grocery store chain. (A data analytics
project typically involves analyzing data to identify trends and patterns
and then using this information to make data-driven decisions.)
Why is quality control/assurance crucial for data engineers in a data
analytics project?
-It ensures that the data is accurate and reliable.
-It ensures that the data is analyzed in a timely manner.
-It ensures that the data is stored in a secure location.
-It ensures that the data is accessible to all stakeholders. - ANSWER It
ensures that the data is accurate and reliable. (Quality control is crucial
for data engineers in a data analytics project because it ensures that the
data used for analysis is accurate and reliable.)
,What is data science?
-A field that involves creating data visualizations to provide insights
-The process of creating computer programs to automate tasks
-The study of how computers interact with human language
-The practice of using statistical methods to extract insights from data -
ANSWER The practice of using statistical methods to extract insights
from data. (Data science is a multidisciplinary field involving various
statistical, mathematical, and computational methods to extract
meaningful insights and knowledge from data.)
How is data science different from data analytics?
-Data science focuses more on data visualization, while data analytics
focuses on data cleaning and preprocessing.
-Data science focuses more on tracking experimental data, and data
analytics is based on statistical methods and hypotheses.
-Data science involves creating new algorithms, while data analytics uses
existing statistical methods.
-Data science focuses on developing new algorithms and models, while
data analytics focuses on using existing models to analyze data. -
ANSWER Data science focuses on developing new algorithms and
models, while data analytics focuses on using existing models to analyze
data. (Data science is more research-based, while data analytics is more
focused on the practical applications of data analytics.)
Which comparison describes the difference between data analytics and
data science?
-Data analytics focuses on statistics, and data science mainly focuses on
qualitative reasoning.
-Data science involves analyzing data from structured sources, while data
analytics involves analyzing data from unstructured sources.
-Data analytics is the process of analyzing data to extract insights, while
data science involves building and testing models to make predictions.
-Data analytics focuses on descriptive analysis, while data science
focuses on prescriptive analysis. - ANSWER Data analytics is the process
of analyzing data to extract insights, while data science involves building
and testing models to make predictions. (Data analytics involves using
statistical and quantitative methods to analyze data to extract insights and
solve problems, while data science involves using machine learning and
statistical models to build predictive models and make decisions based on
data.)
, What does a data analyst do in a data analytics project?
-Focuses on building machine learning models
-Conducts exploratory data analysis to identify trends and patterns
-Designs and develops databases and data pipelines
-Oversees data governance and data quality assurance - ANSWER
Conducts exploratory data analysis to identify trends and patterns. (Data
analysts are responsible for analyzing data to identify trends and patterns
that can inform business decisions. This typically involves conducting
exploratory data analysis, which involves visually exploring and
summarizing data to identify patterns and relationships.)
What is the function of a data scientist in an organization?
-To oversee data governance and compliance
-To work independently to analyze data and make decisions based on
their findings
-To conduct statistical analysis and machine learning modeling
-To design and maintain data visualizations and dashboards - ANSWER
To conduct statistical analysis and machine learning modeling. (Data
scientists analyze complex datasets using statistical analysis and machine
learning techniques. This typically involves cleaning and preprocessing
data, conducting exploratory data analysis, building and testing models,
and communicating insights to business stakeholders.)
What is the role of a business intelligence analyst?
-Designing and maintaining data visualizations and dashboards
-Conducting statistical analysis and machine learning modeling
-Developing and implementing data processing pipelines
-Overseeing data governance and compliance - ANSWER Designing and
maintaining data visualizations and dashboards. (Business intelligence
analysts are responsible for designing and maintaining data visualizations
and dashboards to communicate business insights to stakeholders.)
What is a primary responsibility of a data engineer?
-Designing and developing data visualizations for stakeholders
-Designing and implementing data storage solutions
-Analyzing and interpreting data to inform business decisions
-Developing predictive models using machine learning algorithms -
ANSWER Designing and implementing data storage solutions. (Data
engineers are responsible for designing and implementing data storage
solutions that enable efficient and effective processing, storage, and
retrieval.)
What is a primary responsibility of a machine learning engineer?
ANALYTICS OA ACTUAL EXAM
2025/2026 COMPLETE
QUESTIONS WITH CORRECT
DETAILED ANSWERS || 100%
GUARANTEED PASS <BRAND
NEW VERSION>
What is Data analytics?
-The process of encrypting data to keep it secure
-The process of storing data in a secure location for future use
-The process of analyzing data to extract insights
-The process of collecting data from various sources - ANSWER The
process of analyzing data to extract insights. (Data analytics involves
analyzing data to extract insights and inform decision-making. This
includes using various techniques and tools to explore, clean, transform,
and model data and visualize and communicate findings.)
Which type of data analytics project aims to determine why something
happened in the past?
-Prescriptive
-Descriptive
-Predictive
-Diagnostic - ANSWER Descriptive (Descriptive analytics focuses on
summarizing past events and understanding what happened.)
What are the different types of data analytics projects?
-Regression analysis, time series analysis, text analytics, and network
analysis
-Data warehousing, data mining, data visualization, and business
intelligence
-Descriptive, diagnostic, predictive, and prescriptive analytics
,-Data collection, data cleaning, data transformation, and data
visualization - ANSWER Descriptive, diagnostic, predictive, and
prescriptive analytics
What is the difference between exploratory and confirmatory data
analytics projects?
-Exploratory projects involve testing hypotheses and finding patterns in
data, while confirmatory projects involve verifying existing hypotheses.
-Exploratory projects involve analyzing data from a single source, while
confirmatory projects involve integrating data from multiple sources.
-Exploratory projects involve analyzing data that is already structured,
while confirmatory projects involve analyzing unstructured data.
-Exploratory projects involve analyzing large datasets, while
confirmatory projects involve analyzing smaller datasets. - ANSWER
Exploratory projects involve testing hypotheses and finding patterns in
data, while confirmatory projects involve verifying existing hypotheses.
(Exploratory data analytics projects are typically used when little is
known about the data or when researchers look for patterns or trends that
may not have been previously identified.)
Which project is considered a data analytics project?
-Developing a recommendation system to suggest new products to
customers based on their past purchases
-Creating a dashboard to visualize sales data and monitor inventory levels
for a grocery store chain
-Building a predictive model to forecast stock prices for a financial
services company
-Designing a database schema to store customer information for a retail
store - ANSWER Creating a dashboard to visualize sales data and
monitor inventory levels for a grocery store chain. (A data analytics
project typically involves analyzing data to identify trends and patterns
and then using this information to make data-driven decisions.)
Why is quality control/assurance crucial for data engineers in a data
analytics project?
-It ensures that the data is accurate and reliable.
-It ensures that the data is analyzed in a timely manner.
-It ensures that the data is stored in a secure location.
-It ensures that the data is accessible to all stakeholders. - ANSWER It
ensures that the data is accurate and reliable. (Quality control is crucial
for data engineers in a data analytics project because it ensures that the
data used for analysis is accurate and reliable.)
,What is data science?
-A field that involves creating data visualizations to provide insights
-The process of creating computer programs to automate tasks
-The study of how computers interact with human language
-The practice of using statistical methods to extract insights from data -
ANSWER The practice of using statistical methods to extract insights
from data. (Data science is a multidisciplinary field involving various
statistical, mathematical, and computational methods to extract
meaningful insights and knowledge from data.)
How is data science different from data analytics?
-Data science focuses more on data visualization, while data analytics
focuses on data cleaning and preprocessing.
-Data science focuses more on tracking experimental data, and data
analytics is based on statistical methods and hypotheses.
-Data science involves creating new algorithms, while data analytics uses
existing statistical methods.
-Data science focuses on developing new algorithms and models, while
data analytics focuses on using existing models to analyze data. -
ANSWER Data science focuses on developing new algorithms and
models, while data analytics focuses on using existing models to analyze
data. (Data science is more research-based, while data analytics is more
focused on the practical applications of data analytics.)
Which comparison describes the difference between data analytics and
data science?
-Data analytics focuses on statistics, and data science mainly focuses on
qualitative reasoning.
-Data science involves analyzing data from structured sources, while data
analytics involves analyzing data from unstructured sources.
-Data analytics is the process of analyzing data to extract insights, while
data science involves building and testing models to make predictions.
-Data analytics focuses on descriptive analysis, while data science
focuses on prescriptive analysis. - ANSWER Data analytics is the process
of analyzing data to extract insights, while data science involves building
and testing models to make predictions. (Data analytics involves using
statistical and quantitative methods to analyze data to extract insights and
solve problems, while data science involves using machine learning and
statistical models to build predictive models and make decisions based on
data.)
, What does a data analyst do in a data analytics project?
-Focuses on building machine learning models
-Conducts exploratory data analysis to identify trends and patterns
-Designs and develops databases and data pipelines
-Oversees data governance and data quality assurance - ANSWER
Conducts exploratory data analysis to identify trends and patterns. (Data
analysts are responsible for analyzing data to identify trends and patterns
that can inform business decisions. This typically involves conducting
exploratory data analysis, which involves visually exploring and
summarizing data to identify patterns and relationships.)
What is the function of a data scientist in an organization?
-To oversee data governance and compliance
-To work independently to analyze data and make decisions based on
their findings
-To conduct statistical analysis and machine learning modeling
-To design and maintain data visualizations and dashboards - ANSWER
To conduct statistical analysis and machine learning modeling. (Data
scientists analyze complex datasets using statistical analysis and machine
learning techniques. This typically involves cleaning and preprocessing
data, conducting exploratory data analysis, building and testing models,
and communicating insights to business stakeholders.)
What is the role of a business intelligence analyst?
-Designing and maintaining data visualizations and dashboards
-Conducting statistical analysis and machine learning modeling
-Developing and implementing data processing pipelines
-Overseeing data governance and compliance - ANSWER Designing and
maintaining data visualizations and dashboards. (Business intelligence
analysts are responsible for designing and maintaining data visualizations
and dashboards to communicate business insights to stakeholders.)
What is a primary responsibility of a data engineer?
-Designing and developing data visualizations for stakeholders
-Designing and implementing data storage solutions
-Analyzing and interpreting data to inform business decisions
-Developing predictive models using machine learning algorithms -
ANSWER Designing and implementing data storage solutions. (Data
engineers are responsible for designing and implementing data storage
solutions that enable efficient and effective processing, storage, and
retrieval.)
What is a primary responsibility of a machine learning engineer?
