1|Page
WGU D204 OBJECTIVE ASSESSMENT AND PA LATEST
2025 TEST BANK| D204 MASTERS OF DATA
ANALYTICS JOURNEY OA & PA EXAM WITH 300
REAL EXAM QUESTIONS AND CORRECT VERIFIED
ANSWERS/ ALREADY GRADED A+ (MOST RECENT!!)
What is a potential problem in the data acquisition/query/collection step?
- ANSWER - Quality and type of data may make access more difficult
What are two potential problems in the Data Cleaning/Wrangling step? -
ANSWER - Some cleaning techniques could dramatically change
data/outcomes
Outliers not dealt with can cause problems with statistical models due to
excessive variability.
What is a potential problem in the data exploration/descriptive statistics
step? - ANSWER - Skipping this step could enable faulty perceptions of
the data which hurt advanced analytics.
What are potential problems in the Predictive Modeling step? -
ANSWER - Too many input variables (predictors) can cause problems
Correlation does not imply causation.
Time series models often need sufficient time data to offer precise
trending.
Predictive model accuracy should be assessed using cross-validation.
pg. 1
,2|Page
What is a potential problem in the data mining/supervised models step? -
ANSWER - Running on entire data is problematic; need to subset data
into training and testing datasets to build models.
What are two potential problems in the reporting and
visualization/dashboards step? - ANSWER - Due to potential large
audience consumption, mistakes can cause bad business decisions and
loss of revenue
Improper scales used in graphs could push for interpretations of the story
that is inaccurate
When can some cleaning techniques that could dramatically change
data/outcomes become a potential problem in the Data Analytics Life
Cycle - ANSWER - Data Cleaning
When outliers that are not dealt with, cause problems with statistical
models due to excessive variability, when can this pose a potential
problem - ANSWER - Data Cleaning
When does an analyst start to understand the basic nature of the data, the
relationships within it, the structure of the dataset, the presence of
outliers and the distribution of data values - ANSWER - Data
Exploration
Which Data Analytics Life Cycle phase uses these tools: Python and R -
ANSWER - Predictive Modeling & Data Mining
pg. 2
,3|Page
Which Data Analytics Life Cycle phase uses these tools: Distributions,
visualization tools and statistical tools (mean, median, mode) -
ANSWER - Data Exploration
Skipping which data analytics life cycle step could cause: faulty
perceptions of the data which hurt advances analytics and analyst will
lack the insight into the structure of the data set - ANSWER - Data
Exploration
Which data analytics life cycle phase allows the analyst to move beyond
describing the data to creating models that enable predictions of
outcomes of interest - ANSWER - Predictive Modeling
Too many input variables can cause problems, correlation does not
imply causation and time series model often need sufficient time data to
offer precise trend information; these are potential problems from which
did data analytics life cycle phase - ANSWER - Predictive Modeling
Which data analytics life cycle phase looks for patterns and correlations
in large sets of data - ANSWER - Data Mining
What graph provides a concise summary of the quartiles of numerical
data and is convenient for detecting outliers and skewness - ANSWER -
Boxplot
pg. 3
, 4|Page
What graph is a colorful graph that can visually show frequency or
interaction using a range of colors - ANSWER - Heat Map
What graph is a two-dimensional graph that is great for visualizing
correlations or relationships - ANSWER - Scatterplot
What data type is numbered and labeled stored in an organized
framework with columns and rows. Ex: SQL, Databases, Excel -
ANSWER - Structured
What data type is loosely organized in categories in categories using
tags. Ex: Emails, CSV, XML, JSON - ANSWER - Semi-Structured
What data type is text heavy, information not organized in a clearly
defined framework. Ex: images, text, videos, audio - ANSWER -
Unstructured
What data type is known as numerical, parametric, interval, continuous,
discrete - ANSWER - Quantitative
Visual analytic engine that makes it easier to create interactive visual
analytics in the form of dashboards - ANSWER - Tableau
A software intermediary that allows two applications to talk to each
other - ANSWER - Application Programming Interface (API)
pg. 4
WGU D204 OBJECTIVE ASSESSMENT AND PA LATEST
2025 TEST BANK| D204 MASTERS OF DATA
ANALYTICS JOURNEY OA & PA EXAM WITH 300
REAL EXAM QUESTIONS AND CORRECT VERIFIED
ANSWERS/ ALREADY GRADED A+ (MOST RECENT!!)
What is a potential problem in the data acquisition/query/collection step?
- ANSWER - Quality and type of data may make access more difficult
What are two potential problems in the Data Cleaning/Wrangling step? -
ANSWER - Some cleaning techniques could dramatically change
data/outcomes
Outliers not dealt with can cause problems with statistical models due to
excessive variability.
What is a potential problem in the data exploration/descriptive statistics
step? - ANSWER - Skipping this step could enable faulty perceptions of
the data which hurt advanced analytics.
What are potential problems in the Predictive Modeling step? -
ANSWER - Too many input variables (predictors) can cause problems
Correlation does not imply causation.
Time series models often need sufficient time data to offer precise
trending.
Predictive model accuracy should be assessed using cross-validation.
pg. 1
,2|Page
What is a potential problem in the data mining/supervised models step? -
ANSWER - Running on entire data is problematic; need to subset data
into training and testing datasets to build models.
What are two potential problems in the reporting and
visualization/dashboards step? - ANSWER - Due to potential large
audience consumption, mistakes can cause bad business decisions and
loss of revenue
Improper scales used in graphs could push for interpretations of the story
that is inaccurate
When can some cleaning techniques that could dramatically change
data/outcomes become a potential problem in the Data Analytics Life
Cycle - ANSWER - Data Cleaning
When outliers that are not dealt with, cause problems with statistical
models due to excessive variability, when can this pose a potential
problem - ANSWER - Data Cleaning
When does an analyst start to understand the basic nature of the data, the
relationships within it, the structure of the dataset, the presence of
outliers and the distribution of data values - ANSWER - Data
Exploration
Which Data Analytics Life Cycle phase uses these tools: Python and R -
ANSWER - Predictive Modeling & Data Mining
pg. 2
,3|Page
Which Data Analytics Life Cycle phase uses these tools: Distributions,
visualization tools and statistical tools (mean, median, mode) -
ANSWER - Data Exploration
Skipping which data analytics life cycle step could cause: faulty
perceptions of the data which hurt advances analytics and analyst will
lack the insight into the structure of the data set - ANSWER - Data
Exploration
Which data analytics life cycle phase allows the analyst to move beyond
describing the data to creating models that enable predictions of
outcomes of interest - ANSWER - Predictive Modeling
Too many input variables can cause problems, correlation does not
imply causation and time series model often need sufficient time data to
offer precise trend information; these are potential problems from which
did data analytics life cycle phase - ANSWER - Predictive Modeling
Which data analytics life cycle phase looks for patterns and correlations
in large sets of data - ANSWER - Data Mining
What graph provides a concise summary of the quartiles of numerical
data and is convenient for detecting outliers and skewness - ANSWER -
Boxplot
pg. 3
, 4|Page
What graph is a colorful graph that can visually show frequency or
interaction using a range of colors - ANSWER - Heat Map
What graph is a two-dimensional graph that is great for visualizing
correlations or relationships - ANSWER - Scatterplot
What data type is numbered and labeled stored in an organized
framework with columns and rows. Ex: SQL, Databases, Excel -
ANSWER - Structured
What data type is loosely organized in categories in categories using
tags. Ex: Emails, CSV, XML, JSON - ANSWER - Semi-Structured
What data type is text heavy, information not organized in a clearly
defined framework. Ex: images, text, videos, audio - ANSWER -
Unstructured
What data type is known as numerical, parametric, interval, continuous,
discrete - ANSWER - Quantitative
Visual analytic engine that makes it easier to create interactive visual
analytics in the form of dashboards - ANSWER - Tableau
A software intermediary that allows two applications to talk to each
other - ANSWER - Application Programming Interface (API)
pg. 4
