Real Time SAS Advanced Predictive Modeling Certification Questions and Online Practice Exams

**01. What is a linear Perceptron?**

**A.** A linear Perceptron is a general linear model.

**B.** A linear Perceptron is a generalized linear model.

**C.** A linear Perceptron is a non-parametric model.

**D.** A linear Perceptron is a nonlinear model.

**Answer: B**

**02.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?**

**A.** 19

**B.** 21

**C.** 22

**D**. 25

**Answer : C**

**03.A predictive model uses a data set that has several variables with missing values. What two problems can arise with this model? (Choose two.)**

**A.** The model will likely be overfit.

**B.** There will be a high rate of collinearity among input variables.

**C.** Fewer observations will be used in the model building process.

**D.** New cases with missing values on input variables cannot be scored without extra data processing.

**Answer : C & D**

**04. 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?**

**A.** The sample means from the validation data set are applied to the training and test data sets.

**B.** The sample means from the training data set are applied to the validation and test data sets.

**C.** The sample means from the test data set are applied to the training and validation data sets.

**D.** The sample means from each partition of the data are applied to their own partition.

**Answer : B**

**05. Refer to the exhibit:**

**For the ROC curve shown, what is the meaning of the area under the curve?**

**A.** percent concordant plus percent tied

**B.** percent concordant plus (.5 * percent tied)

**C.** percent concordant plus (.5 * percent discordant)

**D.** percent discordant plus percent tied

**Answer : B**

**06. Refer to the Eigenvalue plot from SAS Enterprise Miner shown below.**

**According to the Kaiser-Guttman method, how many principal components should be retained?**

**A.** 6

**B.** 4

**C.** 10

**D.** 1

**Answer : B**

**07. Refer to the fit summary from SAS Visual Statistics in the exhibit below.**

**What can be concluded from the fit summary?**

**A.** Customer Value Level is not a significant predictor in this model.

**B.** Customer Value Level C has no important variables associated with it.

**C.** Average Sales is a significant predictor when Customer Value Level = E.

**D.** Average Sales is an important predictor when Customer Value Level = C.

**Answer : D.**

**08.What is the maximum number of response variables that SAS Visual Statistics allows for a decision tree?**

Enter your numeric answer in the space below.

________

**Answer : A**

**09. Which statement is true for negative binomial and Poisson regression models?**

**A.** Poisson regression models are used for count data, and negative binomial models are used for binary responses.

**B.** The canonical link function for Poisson regression is the log, while for negative binomial it is the logit.

**C.** Negative binomial models accommodate negative integers while Poisson regression does not.

**D.** Poisson regression is a special case of negative binomial regression.

**Answer : D**

**10. Which software does the SAS Enterprise Miner Open Source Integration node use to execute R programs?**

**A.** SAS/IML

**B. **SAS/STAT

**C.** SAS/ACCESS

**D.** SAS/OR

**Answer : A**

Real Time SAS Advanced Predictive Modeling Certification Questions and Online Practice Exams