Question 1

You are working on a predictive model and you notice that your categorical variable "Color" has 50 different levels, which is adding complexity to your model. You decide to group these levels into fewer categories based on their frequency of occurrence to improve the model’s interpretability and performance. What technique would you most likely use for this task?

Options :

A :

Principal Component Analysis (PCA)

B :

Decision Tree Binning

C :

Top-N Binning

D :

One-hot Encoding

Answer: C

Question 2

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 3

You are developing a predictive model using SAS and have completed the model training phase. You are now preparing to deploy the model for scoring a new dataset. Which mode should you operate in to score the new dataset and why?

Options :

A :

Training mode, because the model needs to be retrained on the new dataset before it can be scored.

B :

Training mode with cross-validation, to ensure that the model generalizes well to the new dataset.

C :

Output mode, as the model is already trained and the goal now is to use the model to predict new outcomes.

D : Output mode, but with added regularization parameters to avoid overfitting on the new dataset.

Answer: C

Question 4

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 5

You need to implement a logistic regression model in SAS to predict customer churn. During model development, you have to ensure the model accounts for the effects of multicollinearity. Which PROC LOGISTIC option should you use to diagnose multicollinearity and provide a measure to identify problematic variables?

Options :

A :

The COLLIN statement to produce collinearity diagnostics

B :

The SELECTION=SCORE option to perform variable selection based on score statistics

C :

The VIF option in the MODEL statement to obtain variance inflation factors

D :

The OUTEST= option to export the estimation results to a dataset

Answer: C

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