WGU D204 Data Analytics Journey Cycle ACTUAL TEST
BANK EXAM 2025-2026 \COMPLETE QUESTIONS AND
CORRECT DETAILED ANSWERS \VERIFIED ANSWERS
\ALREADY GRADED A+ ||ASSURED PASS
Which Data Analytics Life Data Cleaning
Cycle phase uses these
tools: Python, R, SQL and
Excel
When can some cleaning Data Cleaning
techniques that could
dramatically change
data/outcomes become a
potential problem in the
Data Analytics Life Cycle
,When outliers that are not Data Cleaning
dealt with, cause
problems with statistical
models due to excessive
variability, when can this
pose a potential problem
When does an analyst Data Exploration
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
Which Data Analytics Life Data Exploration
Cycle phase uses these
tools: Distributions,
visualization tools and
statistical tools (mean,
median, mode)
Skipping which data Data Exploration
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
Which data analytics life Predictive Modeling
cycle phase allows the
analyst to move beyond
describing the data to
creating models that
enable predictions of
outcomes of interest
, Which Data Analytics Life Predictive Modeling & Data Mining
Cycle phase uses these
tools: Python and R
Too many input variables Predictive Modeling
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
Which data analytics life Data Mining
cycle phase looks for
patterns and correlations
in large sets of data
Data Mining is also known Machine Learning
as...
What is the in-depth step Data Mining
to discover patterns using
automated methods
Running on entire data is Data Mining
problematic, needs to
subset data into training
and testing datasets to
build models and too little
sample could cause
limited insight; these are
potential problems from
which did data analytics
life cycle phase
BANK EXAM 2025-2026 \COMPLETE QUESTIONS AND
CORRECT DETAILED ANSWERS \VERIFIED ANSWERS
\ALREADY GRADED A+ ||ASSURED PASS
Which Data Analytics Life Data Cleaning
Cycle phase uses these
tools: Python, R, SQL and
Excel
When can some cleaning Data Cleaning
techniques that could
dramatically change
data/outcomes become a
potential problem in the
Data Analytics Life Cycle
,When outliers that are not Data Cleaning
dealt with, cause
problems with statistical
models due to excessive
variability, when can this
pose a potential problem
When does an analyst Data Exploration
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
Which Data Analytics Life Data Exploration
Cycle phase uses these
tools: Distributions,
visualization tools and
statistical tools (mean,
median, mode)
Skipping which data Data Exploration
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
Which data analytics life Predictive Modeling
cycle phase allows the
analyst to move beyond
describing the data to
creating models that
enable predictions of
outcomes of interest
, Which Data Analytics Life Predictive Modeling & Data Mining
Cycle phase uses these
tools: Python and R
Too many input variables Predictive Modeling
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
Which data analytics life Data Mining
cycle phase looks for
patterns and correlations
in large sets of data
Data Mining is also known Machine Learning
as...
What is the in-depth step Data Mining
to discover patterns using
automated methods
Running on entire data is Data Mining
problematic, needs to
subset data into training
and testing datasets to
build models and too little
sample could cause
limited insight; these are
potential problems from
which did data analytics
life cycle phase
