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WGU D491 INTRODUCTION TO ANALYTICS OA EXAM with questions and answers 2024
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WGU D491 INTRODUCTION TO ANALYTICS OA EXAM with questions and answers 2024
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WGU D491 INTRODUCTION TO ANALYTICS OAEXAM with questions and answers 2024
What role do stakeholders play in the project cycle?
-Create the project plan and schedule
-Execute the project tasks
-Provide guidance and feedback throughout the project
-Define the project scope and objectives - Provide guidance and feedback throughout
the project. (Stakeholders play a critical role in providing guidance and feedback
throughout the project.)
Which stakeholder should conduct literature reviews for a data analytics project?
-Researcher
-End use
-Database administrator
-Project sponsor - Researcher (Researchers are responsible for thoroughly reviewing
existing literature to identify relevant research and data that can inform the project's
objectives and research questions.)
Why is a project sponsor a key stakeholder in a data analytics project?
-They are the primary users of the project's outputs.
-They provide funding for the project.
-They are responsible for implementing the project.
-They ensure that the project aligns with business goals and objectives. - They ensure
that the project aligns with business goals and objectives. (A project sponsor is a person
or group that provides direction and support to a project. In a data analytics project, the
project sponsor is critical in ensuring the project aligns with the business goals and
objectives.)
How does a data analyst interact with stakeholders during a data analytics project?
-By making decisions on behalf of stakeholders
-By presenting data analysis results in an easily understandable format
-By delegating tasks to stakeholders
-By providing technical details of data analysis methods - By presenting data analysis
results in an easily understandable format. (During a data analytics project, a data
analyst interacts with stakeholders by presenting the data analysis results in an easily
understandable format.)
What role does a project manager play within a data analytics project?
-Provide funding and resources for the project
- Collect and analyze data
-Interpret the project results and make recommendations for future projects
,-Oversee the project team and ensure the project is completed on time and within
budget - Oversee the project team and ensure the project is completed on time and
within budget.
How do stakeholders interact with data analytics projects?
-By providing funding for the project
-By providing finances to complete data visualizations
-By providing consultations at the start of the project
-By providing input throughout the project lifecycle - By providing input throughout the
project lifecycle. (Stakeholders provide input throughout the project lifecycle and may
make key decisions. They may provide input on project requirements, goals, and
priorities and make key decisions throughout the project lifecycle.)
Why are financial operation stakeholders important in a data analytics project?
-They help design and implement data analytics projects.
-They are responsible for data cleaning and migration within a project.
-They provide financial resources for the project.
-They interpret data and provide insights to improve financial performance. - They
interpret data and provide insights to improve financial performance. (Financial
operation stakeholders have a deep understanding of financial performance and provide
insights on how to interpret and improve financial data, trends, and patterns.)
In which phase of the data mining process does the data science team investigate the
problem, develop context and understanding, learn about available data sources, and
formulate initial hypotheses?
-Data preparation
-Model planning
-Model execution
-Discovery - Discovery (This is the stage where the team delves into the problem, gains
insights, learns about the data that can be used, and comes up with initial ideas to be
tested with the data.)
What is the primary purpose of the discovery phase in the data science process?
-To develop interactive visualizations for stakeholder presentations
-To evaluate and optimize data-driven predictive models
-To clean and preprocess the data for analysis
-To understand the business problem and develop initial hypotheses - To understand
the business problem and develop initial hypotheses. (This phase focuses on
investigating the issue, gaining a deeper understanding of the context, learning about
available data sources, and formulating initial ideas that will be tested using data.)
ETLT - Extract, Transform, Load, Transform
Which question of interest is appropriate for a data analytics project to increase a store's
sales?
-Which customer segments will most likely respond to a marketing campaign?
, -Should the store expand to a new location?
-How can the store's social media presence be improved?
-What are the store's best-selling products? - Which customer segments will most likely
respond to a marketing campaign? (This question focuses on identifying customer
segments most likely to respond positively to a marketing campaign, directly addressing
the goal of increasing sales. By targeting the right customer segments, the store can
optimize its marketing efforts and increase its overall sales.)
A data analyst works at an e-commerce company that wants to understand its customer
churn rate. Their manager has tasked them with conducting a data analytics project to
identify customers at risk of churn and offer these customers targeted promotions to
retain their business.
What is the primary purpose of the data analytics project's results in this scenario?
-To identify customer preferences
-To predict customer churn risk
-To optimize inventory management
-To compare the company's churn rate to industry benchmarks - To predict customer
churn risk (The project aims to predict which customers are likely to leave the company
so that targeted promotions can be offered to retain their business.)
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 - 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.)
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 - 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.
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