WPC 300 MIDTERM 1 EXAM
QUESTIONS AND ANSWERS 100% PASS
2026/2027
Heuristic - ANS Learns by acting, uses trial and error, values experiences, relies on common
sense, seeks satisfying solutions
Analytic - ANS Learns by analyzing, uses step by step procedure, values quantitative and
models, builds mathematical models, seeks optimal solution
Data extraction - ANS Extract data from primary/secondary source
Data transformation - ANS Transform/clean data into proper format or structure for the
purpose of querying and analysis
Data load - ANS Load data into final target database, more specifically as operational data
store, data mart or data warehouse
Descriptive Modeling - ANS Basic plots of variables
Find patterns in data
Descriptive statistics
@2026 ALLRIGHTS RESERVED 1
, Correlations
Outliers
Sampling
Some data mining techniques - clustering
Explanatory modeling - ANS Develop model to understand a (causal) relationship
Predictive analysis - ANS Linear regression
Data mining
Classification
Decision tree
Association rule (market basket analysis)
Text mining
Simulation
Problem Framing - ANS Tell an interesting and complete story, find an appropriate solution
framework, routinize the procedure
Agile - ANS Empirical process control
Waterfall - ANS Defined process control
Hypothesis - ANS Educated Guess
Data - ANS Discrete, unorganized, raw facts
@2026 ALLRIGHTS RESERVED 2
QUESTIONS AND ANSWERS 100% PASS
2026/2027
Heuristic - ANS Learns by acting, uses trial and error, values experiences, relies on common
sense, seeks satisfying solutions
Analytic - ANS Learns by analyzing, uses step by step procedure, values quantitative and
models, builds mathematical models, seeks optimal solution
Data extraction - ANS Extract data from primary/secondary source
Data transformation - ANS Transform/clean data into proper format or structure for the
purpose of querying and analysis
Data load - ANS Load data into final target database, more specifically as operational data
store, data mart or data warehouse
Descriptive Modeling - ANS Basic plots of variables
Find patterns in data
Descriptive statistics
@2026 ALLRIGHTS RESERVED 1
, Correlations
Outliers
Sampling
Some data mining techniques - clustering
Explanatory modeling - ANS Develop model to understand a (causal) relationship
Predictive analysis - ANS Linear regression
Data mining
Classification
Decision tree
Association rule (market basket analysis)
Text mining
Simulation
Problem Framing - ANS Tell an interesting and complete story, find an appropriate solution
framework, routinize the procedure
Agile - ANS Empirical process control
Waterfall - ANS Defined process control
Hypothesis - ANS Educated Guess
Data - ANS Discrete, unorganized, raw facts
@2026 ALLRIGHTS RESERVED 2
