[PDF] SAS Advanced Analytics Professional (A00-225) Certification Exam

1. How to Prepare for SAS Advanced Analytics Professional A00-225 Certification? A00-225 Certification Made Easy with AnalyticsExam.com.

2. SAS A00-225 Exam Summary: Exam Name SAS Advanced Predictive Modeling A00-225 Exam Code 50 to 55 Multiple choices or short answer questions No. of Questions 67% Passing Score 110 minutes Time Limit $180 (USD) Exam Fees SAS Advanced Analytics Professional Certification Practice Exam Online Practice Test SAS Advanced Analytics Professional Certification Sample Question Sample Questions Rise & Shine with AnalyticsExam.com

3. SAS Advanced Analytics Professional Syllabus Content: Syllabus Topics: Neural Networks - 20% Logistic Regression - 30% Predictive Analytics on Big Data - 40% Open Source Models in SAS - 10% Rise & Shine with AnalyticsExam.com

4. SAS Advanced Analytics Professional Training: Recommended Training: Using SAS to Put Open Source Models into Production SAS Enterprise Miner High-Performance Data Mining Nodes Predictive Modeling Using SAS In-Memory Statistics SAS Visual Statistics: Interactive Model Building Predictive Modeling Using Logistic Regression Neural Network Modeling Rise & Shine with AnalyticsExam.com

5. Tips to Prepare for A00-225 ● Understand the all Syllabus Topics. ● Perform SAS Advanced Analytics Professional online test at AnalyticsExam.com. ● Identify your weak areas from SAS Advanced Analytics Professional mock test and asses yourself frequently. Rise & Shine with AnalyticsExam.com

6. SAS A00-225 Sample Questions Rise & Shine with AnalyticsExam.com

7. Que.: 1 What is a linear Perceptron? Options: 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. Rise & Shine with AnalyticsExam.com

8. Answer: b) A linear Perceptron is a generalized linear model. Rise & Shine with AnalyticsExam.com

9. Que.: 2 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) 22 d) 25 Rise & Shine with AnalyticsExam.com

10. Answer: c) 22 Rise & Shine with AnalyticsExam.com

11. Que.: 3 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 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. Rise & Shine with AnalyticsExam.com

12. Answer: b) The sample means from the training data set are applied to the validation and test data sets. Rise & Shine with AnalyticsExam.com

13. Que.: 4 Which software does the SAS Enterprise Miner Open Source Integration node use to execute R programs? Options: a) SAS/IML b) SAS/STAT c) SAS/ACCESS d) SAS/OR Rise & Shine with AnalyticsExam.com

14. Answer: a) SAS/IML Rise & Shine with AnalyticsExam.com

15. Que.: 5 Which statement is true for negative binomial and Poisson regression models? Options: 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. Rise & Shine with AnalyticsExam.com

16. Answer: d) Poisson regression is a special case of negative binomial regression. Rise & Shine with AnalyticsExam.com

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