# SAS Advanced Analytics Professional (A00-225) Certification Exam Sample Questions

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## SAS A00-225 Sample Questions:

Q 1: What is the maximum number of response variables that SAS Visual Statistics allows for a decision tree?
Options:
A: 2
B: 4
C: 1
D: 3

Q 2: When mean imputation is performed on data after the data is partitioned for honest assessment, what is the most appropriate method for handling the mean imputation?
Options:
A: The sample means from the training data set are applied to the validation and test data sets.
B: The sample means from the validation data set are applied to the training and test data sets.
C: The sample means from each partition of the data are applied to their own partition.
D: The sample means from the test data set are applied to the training and validation data sets.

Q 3: What is a linear Perceptron?
Options:
A: A linear Perceptron is a non-parametric model.
B: A linear Perceptron is a nonlinear model.
C: A linear Perceptron is a general linear model.
D: A linear Perceptron is a generalized linear model.

Q 4: A predictive model uses a data set that has several variables with missing values. What two problems can arise with this model? (Choose two.)
Options:
A: The model will likely be overfit.
B: There will be a high rate of collinearity among input variables.
C: New cases with missing values on input variables cannot be scored without extra data processing.
D: Fewer observations will be used in the model building process.

Q 5: Consider a Generalized Additive Neural Network (GANN) with 3 continuous inputs and 2 hidden nodes for each input. How many parameters do you need to estimate when training the neural network?
Options:
A: 19
B: 21
C: 25
D: 22

Q 6: Refer to the fit summary from SAS Visual Statistics in the exhibit below.

What can be concluded from the fit summary?
Options:
A: Average Sales is a significant predictor when Customer Value Level = E.
B: Customer Value Level C has no important variables associated with it.
C: Average Sales is an important predictor when Customer Value Level = C.
D: Customer Value Level is not a significant predictor in this model.