# Free Share A00-240 Exam Dumps and Practice Questions and Answers

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Question 1:

Refer to the ROC curve: As you move along the curve, what changes?

A. The priors in the population

B. The true negative rate in the population

C. The proportion of events in the training data

D. The probability cutoff for scoring

Question 2:

When mean imputation is performed on data after the data is partitioned for honest assessment, what is the most appropriate method for handling the mean imputation?

A. The sample means from the validation data set are applied to the training and test data sets.

B. The sample means from the training data set are applied to the validation and test data sets.

C. The sample means from the test data set are applied to the training and validation data sets.

D. The sample means from each partition of the data are applied to their own partition.

Question 3:

Refer to the lift chart: At a depth of 0.1, Lift = 3.14. What does this mean?

A. Selecting the top 10% of the population scored by the model should result in 3.14 times more events than a random draw of 10%.

B. Selecting the observations with a response probability of at least 10% should result in 3.14 times more events than a random draw of 10%.

C. Selecting the top 10% of the population scored by the model should result in 3.14 times greater accuracy than a random draw of 10%.

D. Selecting the observations with a response probability of at least 10% should result in 3.14 times greater accuracy than a random draw of 10%.

Question 4:

Assume a \$10 cost for soliciting a non-responder and a \$200 profit for soliciting a responder. The logistic regression model gives a probability score named P_R on a SAS data set called VALID. The VALID data set contains the responder variable Pinch, a 1/0 variable coded as 1 for responder. Customers will be solicited when their probability score is more than 0.05.

Which SAS program computes the profit for each customer in the data set VALID? A. Option A

B. Option B

C. Option C

D. Option D

Question 5:

In order to perform honest assessment on a predictive model, what is an acceptable division between training, validation, and testing data?

A. Training: 50% Validation: 0% Testing: 50%

B. Training: 100% Validation: 0% Testing: 0%

C. Training: 0% Validation: 100% Testing: 0%

D. Training: 50% Validation: 50% Testing: 0%

Question 6:

A marketing campaign will send brochures describing an expensive product to a set of customers. The cost for mailing and production per customer is \$50. The company makes \$500 revenue for each sale. What is the profit matrix for a typical person in the population? A. Option A

B. Option B

C. Option C

D. Option D

Question 7:

The total modeling data has been split into training, validation, and test data. What is the best data to use for model assessment?

A. Training data

B. Total data

C. Test data

D. Validation data

Question 8:

What is a drawback to performing data cleansing (imputation, transformations, etc.) on raw data prior to partitioning the data for honest assessment as opposed to performing the data cleansing after partitioning the data?

A. It violates assumptions of the model.

B. It requires extra computational effort and time.

C. It omits the training (and test) data sets from the benefits of the cleansing methods.

D. There is no ability to compare the effectiveness of different cleansing methods.

Question 9:

A company has branch offices in eight regions. Customers within each region are classified as either “High Value” or “Medium Value” and are coded using the variable name VALUE. In the last year, the total amount of purchases per customer is used as the response variable.

Suppose there is a significant interaction between REGION and VALUE. What can you conclude?

A. More high value customers are found in some regions than others.

B. The difference between average purchases for medium and high value customers depends on the region.

C. Regions with higher average purchases have more high value customers.

D. Regions with higher average purchases have more medium value customers.

Question 10:

Given the following GLM procedure output: Which statement is correct at an alpha level of 0.05?

A. School*Gender should be removed because it is non-significant.

B. Gender should be removed because it is non-significant.

C. School should be removed because it is significant.

D. Gender should not be removed due to its involvement in the significant interaction.

Question 11:

There are missing values in the input variables for a regression application.

Which SAS procedure provides a viable solution?

A. GLM

B. VARCLUS

C. STDI2E

D. CLUSTER

Question 12:

Screening for non-linearity in binary logistic regression can be achieved by visualizing:

A. A scatter plot of binary response versus a predictor variable.

B. A trend plot of empirical logit versus a predictor variable.

C. A logistic regression plot of predicted probability values versus a predictor variable.

D. A box plot of the odds ratio values versus a predictor variable.

Question 13:

Given the following SAS data set TEST: Which SAS program is NOT a correct way to create dummy variables? A. Option A

B. Option B

C. Option C

D. Option D

Question 14:

An analyst fits a logistic regression model to predict whether or not a client will default on a loan. One of the predictors in the model is agent, and each agent serves 15-20 clients each. The model fails to converge. The analyst prints the summarized data, showing the number of defaulted loans per agent. See the partial output below: What is the most likely reason that the model fails to converge?

A. There is quasi-complete separation in the data.

B. There is collinearity among the predictors.

C. There are missing values in the data.

D. There are too many observations in the data.

Question 15:

Including redundant input variables in a regression model can:

A. Stabilize parameter estimates and increase the risk of overfitting.

B. Destabilize parameter estimates and increase the risk of overfitting.

C. Stabilize parameter estimates and decrease the risk of overfitting.

D. Destabilize parameter estimates and decrease the risk of overfitting.