Refer to the exhibit.

You have created a density plot of purchase amountsfrom a retail website as shown. What should you do
next?

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 wouldyou 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 alsoshows 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 alsoshows 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 functioncan 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 yourdata, 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 associationrules:
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 datasetbased on rules with confidence greater than 60%.
Which of the rules will be kept in the analysis?