WPC 300 COMPREHENSIVE EXAM 2026/2027 QUESTIONS
AND SOLUTIONS RATED A+
✔✔Cubes - ✔✔Made up of cells organized by dimensions and contain measures
✔✔MDA: Dice - ✔✔Subset of a cube. All 3 dimensions but you only pick a few
measures
✔✔MDA: Pivot - ✔✔Rotate current view of data
✔✔MDA: Drill-down - ✔✔Moving from aggregate data to granular data (hierarchy)
✔✔MDA: Roll-up - ✔✔Moving from granular to aggregate data. Lower to higher in
hierarchy
✔✔Decision Tree - ✔✔Cascading if-then statements
✔✔mutually exclusive - ✔✔Events that cannot occur at the same time.
✔✔Collectively Exhaustive - ✔✔at least one of the events must occur when an
experiment is conducted
✔✔Salmon Logic Tree - ✔✔The influencing factors were too small and were pruned.
✔✔Cleaving framework - ✔✔Use exisiting frameworks and theories to breakdown and
disaggregate data
Ex of Frame) Price/volume
Ex of Elements) Marketshare
✔✔Teamwork in Disaggregation process - ✔✔Avoids attachment in prob prioritization,
see dif lenses, cleaving frames and valuable insights.
✔✔Categorical data - ✔✔nominal and ordinal
✔✔Numerical - ✔✔interval and ratio
✔✔Nominal data - ✔✔Data which consists of names, labels, or categories.
✔✔Ordinal data - ✔✔data exists in categories that are ordered but differences cannot
be determined or they are meaningless. (Example: 1st, 2nd, 3rd)
✔✔Interval data - ✔✔Differences between values can be found, but there is no absolute
0. (Temp. and Time)
, ✔✔Ratio data - ✔✔data with an absolute 0. Ratios are meaningful. (Length, Width,
Weight, Distance)
✔✔Continuous data variable - ✔✔Variable can assume an infinite # of real values w/i a
given interval. Can have decimals
Ex.) Height 6.01 or 6.0001 feet
✔✔Discrete Data variable - ✔✔a variable that is limited to a finite number of values;
data for such a variable.
Ex.) Survey w/ scale from 1-10
✔✔Centrality - ✔✔where is most data located. Mean median mode
✔✔Spread - ✔✔Distribution of data. Range, standard deviation, variance
✔✔Shape - ✔✔describes graph's pattern. Skewness, kurtosis
✔✔Normal Probability Distribution Function - ✔✔68.26% of data falls within +1 or -1
standard deviation
95% falls within +2 or -2 standarad deviation from mean
99.7% within +3 or - 3
✔✔Student's t distribution function - ✔✔Used for small sample sizes. More likely to
have data fall far from its mean
✔✔Poisson Distribution function - ✔✔Calculates the probability of an event. The
amount of lambda λ (average events) determines the difference graph. Applied to
discrete random variables
✔✔interquartile range - ✔✔The difference between the upper and lower quartiles. Q3-
Q1
✔✔Cumulative probability function - ✔✔tells you the sum of all the individual
probabilities up to and including the given value of x. Total area under prob curve from 0
to probability value
✔✔Central Limit Theorem - ✔✔The theory that, as sample size increases, the
distribution of sample means of size n, randomly selected, approaches a normal
distribution.
✔✔Z-score - ✔✔(x-mean)/standard deviation
✔✔Bernoulli Distribution - ✔✔Discrete distribution with 2 possible outcomes
Ex.) Flipping a coin
AND SOLUTIONS RATED A+
✔✔Cubes - ✔✔Made up of cells organized by dimensions and contain measures
✔✔MDA: Dice - ✔✔Subset of a cube. All 3 dimensions but you only pick a few
measures
✔✔MDA: Pivot - ✔✔Rotate current view of data
✔✔MDA: Drill-down - ✔✔Moving from aggregate data to granular data (hierarchy)
✔✔MDA: Roll-up - ✔✔Moving from granular to aggregate data. Lower to higher in
hierarchy
✔✔Decision Tree - ✔✔Cascading if-then statements
✔✔mutually exclusive - ✔✔Events that cannot occur at the same time.
✔✔Collectively Exhaustive - ✔✔at least one of the events must occur when an
experiment is conducted
✔✔Salmon Logic Tree - ✔✔The influencing factors were too small and were pruned.
✔✔Cleaving framework - ✔✔Use exisiting frameworks and theories to breakdown and
disaggregate data
Ex of Frame) Price/volume
Ex of Elements) Marketshare
✔✔Teamwork in Disaggregation process - ✔✔Avoids attachment in prob prioritization,
see dif lenses, cleaving frames and valuable insights.
✔✔Categorical data - ✔✔nominal and ordinal
✔✔Numerical - ✔✔interval and ratio
✔✔Nominal data - ✔✔Data which consists of names, labels, or categories.
✔✔Ordinal data - ✔✔data exists in categories that are ordered but differences cannot
be determined or they are meaningless. (Example: 1st, 2nd, 3rd)
✔✔Interval data - ✔✔Differences between values can be found, but there is no absolute
0. (Temp. and Time)
, ✔✔Ratio data - ✔✔data with an absolute 0. Ratios are meaningful. (Length, Width,
Weight, Distance)
✔✔Continuous data variable - ✔✔Variable can assume an infinite # of real values w/i a
given interval. Can have decimals
Ex.) Height 6.01 or 6.0001 feet
✔✔Discrete Data variable - ✔✔a variable that is limited to a finite number of values;
data for such a variable.
Ex.) Survey w/ scale from 1-10
✔✔Centrality - ✔✔where is most data located. Mean median mode
✔✔Spread - ✔✔Distribution of data. Range, standard deviation, variance
✔✔Shape - ✔✔describes graph's pattern. Skewness, kurtosis
✔✔Normal Probability Distribution Function - ✔✔68.26% of data falls within +1 or -1
standard deviation
95% falls within +2 or -2 standarad deviation from mean
99.7% within +3 or - 3
✔✔Student's t distribution function - ✔✔Used for small sample sizes. More likely to
have data fall far from its mean
✔✔Poisson Distribution function - ✔✔Calculates the probability of an event. The
amount of lambda λ (average events) determines the difference graph. Applied to
discrete random variables
✔✔interquartile range - ✔✔The difference between the upper and lower quartiles. Q3-
Q1
✔✔Cumulative probability function - ✔✔tells you the sum of all the individual
probabilities up to and including the given value of x. Total area under prob curve from 0
to probability value
✔✔Central Limit Theorem - ✔✔The theory that, as sample size increases, the
distribution of sample means of size n, randomly selected, approaches a normal
distribution.
✔✔Z-score - ✔✔(x-mean)/standard deviation
✔✔Bernoulli Distribution - ✔✔Discrete distribution with 2 possible outcomes
Ex.) Flipping a coin
