Question 1

You are conducting a time series analysis and need to estimate the parameters of an ARIMA (Autoregressive Integrated Moving Average) model to forecast future sales. Given that the data show signs of non-stationarity and seasonality, which parameter estimation method should you use for the best results?

Options :

A :

Ordinary Least Squares (OLS)

B :

Maximum Likelihood Estimation (MLE)

C :

Generalized Least Squares (GLS)

D :

Instrumental Variables Estimation (IVE)

Answer: B

Question 2

When integrating SAS and R to perform advanced predictive modeling, it's crucial to correctly set up the SAS environment to communicate with R. Assuming that the R software is properly installed on the same machine as SAS, which of the following steps is essential to allow SAS to properly connect to and execute R scripts?

Options :

A :

Set the RHOME system option to the path where R is installed.

B :

Execute the 'library(SASxport)' command in R before calling it from SAS.

C :

Ensure that SAS is installed with the R integration option selected during setup.

D :

Update the PATH system variable to include the directory where the Rscript executable is located.

Answer: A

Question 3

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 4

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 5

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

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