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WGU D491 (Introduction to Data Analytics D491) 2024 Questions With 100% Correct Answers!!
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WGU D491 (Introduction to Data Analytics D491) 2024 Questions With 100% Correct Answers!!
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Name: Score:229 Multiple choice questions
Term 1 of 229
What is the most appropriate analytics technique for predicting sales for the next quarter?
Bar chart
Tree map
Heat map
Regression analysis
Definition 2 of 229
BI is an explanatory tactic that looks at the present and past of a business's' operations to gauge
how it is doing (i.e. questions such as: What happened last quarter? Where is the problem? How
many units sold? etc).
Big data, on the other hand has this capability but can also be employed for predictive modelling,
forecasting, optimization, and statistical analysis (i.e. questions such as what if...? What will happen
next? What if these trends continue? Why is this happening? etc.).
BI - provides reports, dashboards, and queries on busines questions for the current period or the
past- uses highly structured data organized in rows and columns for accurate reporting
DS - uses data about the present to explore informed decision making about the future- tend to
use many types of data sources, including large or unconventional datasets
In which phase would the team expect to invest most of the project time? Why? Where
would the team expect to spend the least time?
Explain the difference between BI and Data Science.
What are association rules used for?
8. What is the definition of topic in topic models?
,Term 3 of 229
Which information tool is a possible source of data in a data analytics project?
Company logo designs
Consumer perception survey questions
Marketing slogans
Corporate information system
Term 4 of 229
What is the purpose of communicating data analytics results to stakeholders?
To demonstrate the value and impact of data analytics on business outcomes
To share technical details and methodologies used in the analysis
To persuade stakeholders to adopt new data analytics tools and techniques
To validate the accuracy and reliability of the data used in the analysis
Term 5 of 229
What role do stakeholders play in the project cycle?
Create the project plan and schedule
Define the project scope and objectives
Execute the project tasks
Provide guidance and feedback throughout the project
,Definition 6 of 229
the probability under the assumption of no effect or no difference (null hypothesis), of obtaining a
result equal to or more extreme than what was actually observed.
p-value
r-squared
z-score
f-statistic
Term 7 of 229
Which task is typically performed to handle outliers during the data preparation phase?
Normalization
Truncating extreme values
Data transformation
Missing data imputation
Term 8 of 229
Which role is required to provide requirements for a project?
Data visualization tools and presentation software
Provide guidance and feedback throughout the project
- Project sponsor
- business user
- data scientist
Analyzing and interpreting data to inform questions
, Term 9 of 229
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
Term 10 of 229
A company recently completed a data analytics project to identify the most energy-efficient
products to add to the catalog. The project team comprised business users, project sponsors,
analysts, data scientists, data engineers, and database administrators. Now, the team needs to
share their findings with various stakeholders. What should the data scientists, data engineer, and
database administrator do to share their findings?
Share code and provide implementation details
Manage project timelines and budgets
Assess the benefits and implications of findings
Create high-level presentations
Term 11 of 229
A data analyst working for a retail company has a team that analyzes customer purchasing
behavior and identifies a segment of high-value customers who have a high propensity to churn.
Now the analyst needs to communicate the results to the customer service department to
operationalize the insights and reduce customer churn. How does the communication of results
tie to the operationalize phase of data analytics?
By conducting further data analysis and exploration
By implementing personalized outreach to customers
By refining data collection processes
By training customer service representatives
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