D204 WGU Study Set Actual Questions and Correct
Answers
Q1
Which of these is NOT a topic of C interest for Discovery/Planning/Business Understanding?
Project Scope Identify stakeholders and research questions/KPIs Build a data pipeline (ETL)
Identify timeline, budget, and participants
Answer: Discovery/Planning/Business Understanding?
Q2
What is a potential problem to A consider in the planning phase? Lack of clear focus on
stakeholders, timeline, limitations, and budget Quality and type of data may make access more
difficult Some cleaning techniques could dramatically change data/outcomes Outliers not dealt
with can cause problems with statistical models due to excessive variability.
Answer: Lack of clear focus on
Q3
In what phase does the analyst deal Data acquisition with the following: Gather/collect data
from a variety of sources Provide structure to data accessible via relational databases (SQL)
Build data pipeline (ETL) Use of API to download data from an external source
Answer: Gather/collect data from a variety of sources
Q4
In what phase does the analyst deal Data cleaning/wrangling/scrubbing/munging with the
following: Fixing improperly formatted values Dealing with duplicates, missing data, and outliers
Data reduction
Answer: Fixing improperly formatted values Dealing with duplicates, missing
Q5
In what phase does the analyst deal with the following: Estimate/project future values or
likelihood of an event. Extend correlations found in EDA to mathematical models
Predict/determine output values based on input values Cross-validation of predictive models to
ensure accuracy.
Answer: Predictive Modeling/Data Modeling/Correlation based models/Regression
models/Time Series
, Q6
In what phase does the analyst deal with the following: Creating training and testing datasets to
build models from Identify/detect patterns Determine if groups (clusters) exist in data Classify
data into groups Create models that "learn" and improve (e.g., machine/deep learning, AI, etc)
Answer: Data Mining/Machine Learning/AI/Supervised, Unsupervised Models
Q7
In what phase does the analyst deal with the following: Tell a story with data Provide a
summary of analytic analysis Provide insights to stakeholders Create insightful graphs that
showcase trends and forecasts
Answer: Reporting and visualization/Dashboards
Q8
What is a potential problem in the data acquisition/query/collection step?
Answer: Quality and type of data may make access more difficult
Q9
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.
Q10
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.
Q11
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.
Answers
Q1
Which of these is NOT a topic of C interest for Discovery/Planning/Business Understanding?
Project Scope Identify stakeholders and research questions/KPIs Build a data pipeline (ETL)
Identify timeline, budget, and participants
Answer: Discovery/Planning/Business Understanding?
Q2
What is a potential problem to A consider in the planning phase? Lack of clear focus on
stakeholders, timeline, limitations, and budget Quality and type of data may make access more
difficult Some cleaning techniques could dramatically change data/outcomes Outliers not dealt
with can cause problems with statistical models due to excessive variability.
Answer: Lack of clear focus on
Q3
In what phase does the analyst deal Data acquisition with the following: Gather/collect data
from a variety of sources Provide structure to data accessible via relational databases (SQL)
Build data pipeline (ETL) Use of API to download data from an external source
Answer: Gather/collect data from a variety of sources
Q4
In what phase does the analyst deal Data cleaning/wrangling/scrubbing/munging with the
following: Fixing improperly formatted values Dealing with duplicates, missing data, and outliers
Data reduction
Answer: Fixing improperly formatted values Dealing with duplicates, missing
Q5
In what phase does the analyst deal with the following: Estimate/project future values or
likelihood of an event. Extend correlations found in EDA to mathematical models
Predict/determine output values based on input values Cross-validation of predictive models to
ensure accuracy.
Answer: Predictive Modeling/Data Modeling/Correlation based models/Regression
models/Time Series
, Q6
In what phase does the analyst deal with the following: Creating training and testing datasets to
build models from Identify/detect patterns Determine if groups (clusters) exist in data Classify
data into groups Create models that "learn" and improve (e.g., machine/deep learning, AI, etc)
Answer: Data Mining/Machine Learning/AI/Supervised, Unsupervised Models
Q7
In what phase does the analyst deal with the following: Tell a story with data Provide a
summary of analytic analysis Provide insights to stakeholders Create insightful graphs that
showcase trends and forecasts
Answer: Reporting and visualization/Dashboards
Q8
What is a potential problem in the data acquisition/query/collection step?
Answer: Quality and type of data may make access more difficult
Q9
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.
Q10
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.
Q11
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.
