Refer to the exhibit.

You are building a decision tree. In this exhibit, four variables are listed with their respective values
of info-gain.
Based on this information, on which attribute would you expect the next split to be in the decision
tree?

Refer to the Exhibit.

In the Exhibit, the table shows the values for the input Boolean attributes “A”, “B”, and “C”. It also
shows the values for the output attribute “class”. Which decision tree is valid for the data?

Refer to the Exhibit.

In the Exhibit, the table shows the values for the input Boolean attributes “A”, “B”, and “C”. It also
shows the values for the output attribute “class”. Which decision tree is valid for the data?

Refer to the exhibit.

You are assigned to do an end of the year sales analysis of 1, 000 different products, based on
the transaction table. Which column in the end of year report requires the use of a window
function?

Refer to the Exhibit.

You are working on creating an OLAP query that outputs several rows of with summary rows of
subtotals and grand totals in addition to regular rows that may contain NULL as shown in the
exhibit. Which function can you use in your query to distinguish the row from a regular row to a
subtotal row?

Refer to the exhibit.

After analyzing a dataset, you report findings to your team:
1. Variables A and C are significantly and positively impacting the dependent variable.
2. Variable B is significantly and negatively impacting the dependent variable.
3. Variable D is not significantly impacting the dependent variable.
After seeing your findings, the majority of your team agreed that variable B should be positively
impacting the dependent variable.
What is a possible reason the coefficient for variable B was negative and not positive?

Refer to the exhibit.

You have run a linear regression model against your data, and have plotted true outcome versus
predicted outcome. The R-squared of your model is 0.75. What is your assessment of the model?

Refer to the exhibit.

You are using K-means clustering to classify customer behavior for a large retailer. You need to
determine the optimum number of customer groups. You plot the within-sum-of-squares (wss)
data as shown in the exhibit. How many customer groups should you specify?

Refer to the exhibit.

Click on the calculator icon in the upper left corner. You are given a list of pre-defined association
rules:
A) RENTER => BAD CREDIT
B) RENTER => GOOD CREDIT
C) HOME OWNER => BAD CREDIT
D) HOME OWNER => GOOD CREDIT
E) FREE HOUSING => BAD CREDIT
F) FREE HOUSING => GOOD CREDIT
For your next analysis, you must limit your dataset based on rules with confidence greater than
60%.
Which of the rules will be kept in the analysis?

Refer to the exhibit.

You are using k-means clustering to discover groupings within a data set. You plot within-sum-ofsquares (wss) of multiple cluster sizes. Based on the exhibit, how many clusters should you use in
your analysis?