WGU D361 - TASK 1 AND 2 COMBINED
FINAL TEST 2026 QUESTIONS WITH
CORRECT ANSWERS GRADED A+
◍ Formal Operational Stage (Piaget).
Answer: adolescence to adulthood, abstract thoughts
◍ Which role is required to provide requirements for a project?.
Answer: - Project sponsor- business user- data scientist
◍ 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 that is already
structured, while confirmatory projects involve analyzing unstructured data.-
Exploratory projects involve analyzing large datasets, while confirmatory
projects involve analyzing smaller datasets.- Exploratory projects involve
analyzing data from a single source, while confirmatory projects involve
integrating data from multiple sources..
Answer: Exploratory projects involve testing hypotheses and finding
patterns in data, while confirmatory projects involve verifying existing
hypotheses.NOT CORRECT
◍ What component of a data analytics project is typically completed by a data
analyst?- To collect and store data for the organization- To make decisions
based on the insights derived from data analysis- To clean and preprocess
data to prepare it for analysis- To design and implement machine learning
algorithms.
Answer: To clean and preprocess data to prepare it for analysis
◍ What do data analytics teams do in the operationalize phase of a data
, analytics project?- Apply data transformations to fix problems with data and
surface information- Communicate project benefits, set up the pilot project,
and deploy in production- Explore data, create model sets, and partition
them into training, validation, and test sets- Translate business problems into
data mining problems and locate appropriate data.
Answer: Communicate project benefits, set up the pilot project, and deploy
in production
◍ Which activities should the data analytics team perform during the model
execution phase of this project? - Creating data visualizations and capturing
essential predictors- Deploying the model and measuring its return on
investment- Generating training and test sets and refining models to enhance
performance- Grouping categorical variables and standardizing numeric
values.
Answer: Generating training and test sets and refining models to enhance
performance
◍ Cognitive Development.
Answer: the development of thinking, problem solving, and memory
◍ Skinner's Theory.
Answer: Theory proposed that we learn language through association,
imitation and reinforcement
◍ Cognitivist Theory.
Answer: A research approach that emphasizes how the human mind
receives, processes, stores, and retrieves information in learning and
retrieving information.
◍ Trust versus Mistrust(Erikson).
Answer: Infants learn basic trust if the world is a secure place where their
basic needs are met
◍ Why is it significant to establish failure criteria for a data analytics project in
the discovery phase?- It ensures that the project team will reach its goals.- It
helps the team determine when it is best to accept the conclusions.- It
, provides a best-case scenario approach.- It guides the team in identifying the
main objectives of the project..
Answer: It helps the team determine when it is best to accept the
conclusions.
◍ Concrete Operational Stage (Piaget).
Answer: from 7 years to 11 years, logical thinking
◍ 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.
◍ Pre-operational Stage (Piaget).
Answer: from 2 years to 7 years, development of language, memory, and
imagination (symbolically)
◍ Vygotsky's language theory.
Answer: Social learning
◍ Random forest analysis.
Answer: a popular machine learning algorithm used for classification and
regression tasks due to its high accuracy, robustness, feature importance,
versatility, and scalability
◍ A pharmaceutical company collected data on patient outcomes for a new
drug it is testing.Which question regarding the source or quality of the
available data is most appropriate to ask before analysis?- Did the data come
from a completely unbiased source?- Was the data collected in secret,
without the knowledge of the doctors?- Was the data collected from
, electronic health records (EHRs) of patients using the drug?- Can data be
excluded to decrease the impact of side effects on the analysis?.
Answer: Was the data collected from electronic health records (EHRs) of
patients using the drug?
◍ A retail grocer wants to use association rules in retail marketing to increase
sales.What would be the impact of using an association rule on sales data?-
By analyzing sales data, the data analyst can apply association rules to
discover frequent item sets, which are groups of items often purchased
together.- By analyzing sales data, the data analyst can apply association
rules to discover stockpiling behavior, which can be used for coupons.- By
analyzing sales data, the data analyst can apply association rules to predict
revenues in the future, which can be used in business strategy.- By
analyzing sales data, the data analyst can apply association rules to discover
rare purchases, which can be used for future product generation..
Answer: By analyzing sales data, the data analyst can apply association rules
to discover frequent item sets, which are groups of items often purchased
together.
◍ Which task is commonly performed to identify and address data quality
issues during the data preparation phase?- Performing data deduplication-
Developing data visualization- Conducting data profiling- Executing data
integration.
Answer: Conducting data profiling
◍ A manufacturing company collected data on production processes,
equipment downtime, and maintenance logs.Which question can a data
analytics project answer using diagnostic analytics?- How can energy
consumption be reduced during production processes without affecting
product quality?- What is the cost per unit of production?- What was the
cause of the production process inefficiency that resulted in a six-hour delay
yesterday?- Can future equipment failure be predicted based on past data?.
Answer: What was the cause of the production process inefficiency that
resulted in a six-hour delay yesterday?
FINAL TEST 2026 QUESTIONS WITH
CORRECT ANSWERS GRADED A+
◍ Formal Operational Stage (Piaget).
Answer: adolescence to adulthood, abstract thoughts
◍ Which role is required to provide requirements for a project?.
Answer: - Project sponsor- business user- data scientist
◍ 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 that is already
structured, while confirmatory projects involve analyzing unstructured data.-
Exploratory projects involve analyzing large datasets, while confirmatory
projects involve analyzing smaller datasets.- Exploratory projects involve
analyzing data from a single source, while confirmatory projects involve
integrating data from multiple sources..
Answer: Exploratory projects involve testing hypotheses and finding
patterns in data, while confirmatory projects involve verifying existing
hypotheses.NOT CORRECT
◍ What component of a data analytics project is typically completed by a data
analyst?- To collect and store data for the organization- To make decisions
based on the insights derived from data analysis- To clean and preprocess
data to prepare it for analysis- To design and implement machine learning
algorithms.
Answer: To clean and preprocess data to prepare it for analysis
◍ What do data analytics teams do in the operationalize phase of a data
, analytics project?- Apply data transformations to fix problems with data and
surface information- Communicate project benefits, set up the pilot project,
and deploy in production- Explore data, create model sets, and partition
them into training, validation, and test sets- Translate business problems into
data mining problems and locate appropriate data.
Answer: Communicate project benefits, set up the pilot project, and deploy
in production
◍ Which activities should the data analytics team perform during the model
execution phase of this project? - Creating data visualizations and capturing
essential predictors- Deploying the model and measuring its return on
investment- Generating training and test sets and refining models to enhance
performance- Grouping categorical variables and standardizing numeric
values.
Answer: Generating training and test sets and refining models to enhance
performance
◍ Cognitive Development.
Answer: the development of thinking, problem solving, and memory
◍ Skinner's Theory.
Answer: Theory proposed that we learn language through association,
imitation and reinforcement
◍ Cognitivist Theory.
Answer: A research approach that emphasizes how the human mind
receives, processes, stores, and retrieves information in learning and
retrieving information.
◍ Trust versus Mistrust(Erikson).
Answer: Infants learn basic trust if the world is a secure place where their
basic needs are met
◍ Why is it significant to establish failure criteria for a data analytics project in
the discovery phase?- It ensures that the project team will reach its goals.- It
helps the team determine when it is best to accept the conclusions.- It
, provides a best-case scenario approach.- It guides the team in identifying the
main objectives of the project..
Answer: It helps the team determine when it is best to accept the
conclusions.
◍ Concrete Operational Stage (Piaget).
Answer: from 7 years to 11 years, logical thinking
◍ 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.
◍ Pre-operational Stage (Piaget).
Answer: from 2 years to 7 years, development of language, memory, and
imagination (symbolically)
◍ Vygotsky's language theory.
Answer: Social learning
◍ Random forest analysis.
Answer: a popular machine learning algorithm used for classification and
regression tasks due to its high accuracy, robustness, feature importance,
versatility, and scalability
◍ A pharmaceutical company collected data on patient outcomes for a new
drug it is testing.Which question regarding the source or quality of the
available data is most appropriate to ask before analysis?- Did the data come
from a completely unbiased source?- Was the data collected in secret,
without the knowledge of the doctors?- Was the data collected from
, electronic health records (EHRs) of patients using the drug?- Can data be
excluded to decrease the impact of side effects on the analysis?.
Answer: Was the data collected from electronic health records (EHRs) of
patients using the drug?
◍ A retail grocer wants to use association rules in retail marketing to increase
sales.What would be the impact of using an association rule on sales data?-
By analyzing sales data, the data analyst can apply association rules to
discover frequent item sets, which are groups of items often purchased
together.- By analyzing sales data, the data analyst can apply association
rules to discover stockpiling behavior, which can be used for coupons.- By
analyzing sales data, the data analyst can apply association rules to predict
revenues in the future, which can be used in business strategy.- By
analyzing sales data, the data analyst can apply association rules to discover
rare purchases, which can be used for future product generation..
Answer: By analyzing sales data, the data analyst can apply association rules
to discover frequent item sets, which are groups of items often purchased
together.
◍ Which task is commonly performed to identify and address data quality
issues during the data preparation phase?- Performing data deduplication-
Developing data visualization- Conducting data profiling- Executing data
integration.
Answer: Conducting data profiling
◍ A manufacturing company collected data on production processes,
equipment downtime, and maintenance logs.Which question can a data
analytics project answer using diagnostic analytics?- How can energy
consumption be reduced during production processes without affecting
product quality?- What is the cost per unit of production?- What was the
cause of the production process inefficiency that resulted in a six-hour delay
yesterday?- Can future equipment failure be predicted based on past data?.
Answer: What was the cause of the production process inefficiency that
resulted in a six-hour delay yesterday?
