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WGU D491 Introduction to Analytics Exam Practice Questions with Verified Solutions – New Modified & Latest Update

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Ace your WGU D491 Introduction to Analytics exam with this comprehensive set of practice questions and verified solutions. This newly modified and updated study guide covers the full spectrum of analytics concepts, including data analytics vs. data science, project lifecycle phases, stakeholder roles, data preparation, modeling, visualization, and statistical techniques. Each question includes a detailed answer explanation to help you master key topics and succeed on exam day.

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WGU D491 – INTRODUCTION TO ANALYTICS
EXAM PRACTICE QUESTIONS WITH VERIFIED
SOLUTIONS NEW MIODIFIED LATEST UPDATE


Which comparison describes the difference between data analytics and data science?



- Data analytics focuses on descriptive analysis, while data science focuses on prescriptive
analysis.

- 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 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.

--ANSWER--Data analytics is the process of analyzing data to extract insights, while data
science involves building and testing models to make predictions.




Which type of data analytics project aims to determine why something happened in the past?



- Diagnostic

- Descriptive

- Predictive

- Prescriptive

--ANSWER--Diagnostic


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,What are the different types of data analytics projects?



- Data warehousing, data mining, data visualization, and business intelligence

- Regression analysis, time series analysis, text analytics, and network analysis

- Data collection, data cleaning, data transformation, and data visualization

- Descriptive, diagnostic, predictive, and prescriptive analytics

--ANSWER--Descriptive, diagnostic, predictive, and prescriptive analytics




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




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,Why is quality control/assurance crucial for data engineers in a data analytics project?



- 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 accurate and reliable.

- It ensures that the data is accessible to all stakeholders.

--ANSWER--It ensures that the data is accurate and reliable.




What is data analytics?



- The process of analyzing data to extract insights

- The process of encrypting data to keep it secure

- The process of storing data in a secure location for future use

- The process of collecting data from various sources

--ANSWER--The process of analyzing data to extract insights




What is data science?



- The practice of using statistical methods to extract insights from data

- 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


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, --ANSWER--The practice of using statistical methods to extract insights from data




How is data science different from data analytics?



- Data science focuses more on tracking experimental data, and data analytics is based on
statistical methods and hypotheses.

- Data science focuses on developing new algorithms and models, while data analytics
focuses on using existing models to analyze data.

- Data science focuses more on data visualization, while data analytics focuses on data
cleaning and preprocessing.

- Data science involves creating new algorithms, while data analytics uses existing statistical
methods.

--ANSWER--Data science focuses on developing new algorithms and models, while data
analytics focuses on using existing models to analyze data.




What does a data analyst do in a data analytics project?



- Conducts exploratory data analysis to identify trends and patterns

- Focuses on building machine learning models

- Oversees data governance and data quality assurance

- Designs and develops databases and data pipelines

--ANSWER--Conducts exploratory data analysis to identify trends and patterns




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