Question 1

You have a decision tree with a depth of 4. During validation, you observe the following characteristics: Leaf nodes at the lower depths have higher confidence for the predicted class but represent a smaller portion of the dataset. Conversely, upper-level nodes cover more of the dataset but with lower confidence levels. Given this scenario, which of the following strategies might help in improving the model’s performance without overfitting?

Options :

A : Increase the minimum number of instances required to split a node.

B : Prune the tree to reduce its depth.

C : Decrease the minimum number of instances required for a node to be considered a leaf.

D : Allow the tree to grow deeper by increasing the maximum depth.

Answer: A

Question 2

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 3

A data analyst is using PROC LOGISTIC to perform logistic regression on a dataset where the response variable is binary. The analyst wants to ensure that the output dataset contains the predicted probabilities for the event of interest along with the total number of events and non-events for each group defined by the CLASS variable. Which combination of statements and options should the analyst use?

Options :

A :

CLASS treatment / DESCENDING; MODEL event(binary) = treatment; OUTPUT OUT=pred_data PREDICTED=probabilities;

B :

CLASS treatment; MODEL event(event='1') = treatment / LINK=LOGIT; OUTPUT OUT=pred_data PREDICTED=probabilities;

C :

CLASS treatment; MODEL event(event='1') = treatment / LINK=LOGIT; OUTPUT OUT=pred_data PREDICTED=probabilities EVENTCOUNT=nwayevents NONEVENTCOUNT=nwaynonevents;

D :

CLASS treatment; MODEL event(binary) = treatment; OUTPUT OUT=pred_data PREDICTED=probabilities / NWAY;

Answer: C

Question 4

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 5

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

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