Question 1

You are building an artificial neural network model to predict the probability that a customer will respond to a marketing campaign. The target variable is binary, indicating a '1' for a response and '0' for no response. Which combination of activation function and error function would be most appropriate for the output layer of this neural network?

Options :

A :

Activation function: Hyperbolic tangent (tanh), Error function: Cross-entropy

B :

Activation function: Softmax, Error function: Mean squared error (MSE)

C :

Activation function: Sigmoid, Error function: Cross-entropy

D :

Activation function: ReLU (Rectified Linear Unit), Error function: Mean absolute error (MAE)

Answer: C

Question 2

You are required to use PROC NEURAL in SAS to construct a neural network model that minimizes overfitting. Which combination of options would be most effective for this purpose?

Options :

A : train=breiman; valdata=myvalid;

B : train=quikprop; hides=2(3);

C : decay=0.001; maxiter=200; train=conjugate;

D : train=resilient; maxiter=500; ridging=0.01;

Answer: C

Question 3

A data scientist is using the SAS system to preprocess data for predictive modeling. They need to convert a character variable named `char_date` in the format 'yyyy-mm-dd' to a SAS date variable. Which INPUT statement below correctly performs this conversion?

Options :

A :

date_var = INPUT(char_date, yymmdd10.);

B :

date_var = INPUT(char_date, date9.);

C :

date_var = INPUT(char_date, yymmddd10.);

D :

date_var = INPUT(char_date, ddmmyy10.);

Answer: A

Question 4

Which of the following link functions is appropriate when using the HP GLM node for a binary outcome variable in a logistic regression model?

Options :

A :

Identity link

B :

Log link

C :

Logit link

D :

Inverse link

Answer: C

Question 5

A binary classifier is used to predict a rare event. Its performance is summarized in the following confusion matrix: | | Predicted Negative | Predicted Positive | |-||| | Actual Negative | 9750 | 250 | | Actual Positive | 25 | 75 | Given the model's performance, what is the False Positive Rate (FPR) of the classifier?

Options :

A :

0.0256

B :

0.03

C :

0.25

D :

0.0025

Answer: A

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