Question 1

You are evaluating the relationship between a binary target variable and a continuous predictor variable using an empirical logit plot. The plot shows that for low values of the predictor, the logit of the target variable is also low, and as the predictor value increases, the logit of the target variable increases linearly. However, for high values of the predictor, the logit of the target variable levels off, forming an S-shape. Which of the following interpretations is correct?

Options :

A :

The predictor variable has no relationship with the target variable.

B :

The relationship between the predictor and the target is linear throughout.

C :

There appears to be a threshold after which the predictor-s impact on the logit diminishes.

D :

The predictor variable is perfectly predicting the target variable.

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

When preparing input data for a predictive model, which of the following issues is likely to cause the most significant problems in the performance of the model?

Options :

A :

The input data contains several variables with a small proportion of missing values that can be easily imputed.

B :

The input data contains a few high-cardinality categorical variables that require encoding.

C :

There is a considerable amount of multicollinearity present among the predictor variables.

D :

The input data has been standardized using z-scores.

Answer: C

Question 4

A data analyst is using the RANDOMFOREST procedure to create a predictive model. They want to specify the number of trees to be generated in the random forest for a robust prediction. Which option in the RANDOMFOREST statement correctly sets the desired number of trees?

Options :

A :

NEST=100

B :

NTREE=100

C :

TREES=100

D :

NTREES=100

Answer: B

Question 5

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

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