Question 1

You have built a logistic regression model using PROC LOGISTIC to predict the probability of customer churn based on several predictors. You want to evaluate the performance of the model by analyzing the Receiver Operating Characteristic (ROC) curve. To score a validation dataset and produce the ROC curve, which statement correctly implements the OUTROC option in the SCORE statement?

Options :

A : SCORE DATA=validation OUT=scored_results OUTROC=roc_curve;

B : PROC LOGISTIC DATA=training; SCORE OUTROC=roc_curve; RUN;

C : PROC LOGISTIC DATA=training; SCORE DATA=validation OUTROC=roc_curve; RUN;

D : SCORE DATA=validation OUT=scored_results; MODEL OUTROC=roc_curve;

Answer: C

Question 2

In cluster analysis using parallel coordinate plots in SAS, how do you interpret a plot where clusters are shown with overlapping lines across multiple axes?

Options :

A : The overlapping lines suggest highly distinctive and well-segregated clusters.

B : The overlapping lines indicate that the clusters may not be well-defined, and there could be similarities between them across multiple variables.

C : The overlapping lines represent outliers and anomalous data points that need to be removed before analysis.

D : The overlapping lines are a result of overplotting and require increased transparency to distinguish clusters.

Answer: B

Question 3

A data scientist is evaluating the performance of a neural network model for a regression problem. After training, the scientist wants to assess the model's ability to generalize to new data. Which of the following metrics is the most appropriate for evaluating the performance of the neural network on a validation dataset?

Options :

A :

Accuracy

B :

Mean Squared Error (MSE)

C :

Area Under the ROC Curve (AUC)

D :

Precision

Answer: B

Question 4

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 5

When preparing to score a new dataset with a predictive model you have previously built, you notice that one of the predictor variables has a higher rate of missing values than in the training set used to build the model. What potential issue should you be wary of?

Options :

A :

The scoring process will automatically become faster due to fewer cases to process.

B :

Predictions made by the model may be less reliable due to the difference in data quality.

C :

The model will automatically discard any new records with missing values in the scoring dataset.

D :

There will be no issues as predictive models are robust to changes in the rate of missing data.

Answer: B

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