A00-406 | SAS Viya Supervised Machine Learning Pipelines | Sample Questions

1. How to Prepare for SAS A00-406 Certification? A00-406 Certification Made Easy with AnalyticsExam.com.

2. SAS A00-406 Exam Summary: Exam Name SAS Viya Supervised Machine Learning Pipelines Exam Code A00-406 No. of Questions 50-55 Passing Score 62% Time Limit 90 minutes Exam Fees $180 (USD) Online Practice Test SAS A00-406 Certification Practice Exam Sample Questions SAS A00-406 Certification Sample Question Rise & Shine with AnalyticsExam.com

3. SAS A00-406 Syllabus Content: Syllabus Topics: ● Data Sources (30 - 36%) ● Building Models (40 - 46%) ● Model Assessment and Deployment Models (24 - 30%) Rise & Shine with AnalyticsExam.com

4. SAS A00-406 Training: Recommended Training: ● Machine Learning with SAS Viya Rise & Shine with AnalyticsExam.com

5. Tips to Prepare for A00-406 ● Understand the all Syllabus Topics. ● Perform SAS Viya Supervised Machine Learning Pipelines online test at AnalyticsExam.com. ● Identify your weak areas from SAS Viya Supervised Machine Learning Pipelines mock test and asses yourself frequently. Rise & Shine with AnalyticsExam.com

6. SAS A00-406 Sample Questions Rise & Shine with AnalyticsExam.com

7. Que.: 1 - Which feature extraction method can take both interval variables and class variables as inputs? Options: a) Autoencoder b) Principal component analysis c) Singular value decomposition d) Robust PCA Rise & Shine with AnalyticsExam.com

8. Answer: a) Autoencoder Rise & Shine with AnalyticsExam.com

9. Que.: 2 - A project has been created and a pipeline has been run in Model Studio. Which project setting can you edit? Options: a) Advisor Options for missing values b) Partition Data percentages c) Rules for model comparison statistic d) Event-based Sampling proportions Rise & Shine with AnalyticsExam.com

10. Answer: c) Rules for model comparison statistic Rise & Shine with AnalyticsExam.com

11. Que.: 3 - In natural language processing (NLP), what is a common preprocessing step for text data before building models? Options: a) Standardization b) Tokenization c) Principal Component Analysis (PCA) d) One-Hot Encoding Rise & Shine with AnalyticsExam.com

12. Answer: b) Tokenization Rise & Shine with AnalyticsExam.com

13. Que.: 4 - What is the difference between a classification problem and a regression problem in machine learning? Options: a) Classification predicts categorical outcomes, while regression predicts numeric outcomes. b) Classification is a type of regression problem. c) Regression predicts categorical outcomes, while classification predicts numeric outcomes. d) There is no difference; the terms are used interchangeably. Rise & Shine with AnalyticsExam.com

14. Answer: a) Classification predicts categorical outcomes, while regression predicts numeric outcomes. Rise & Shine with AnalyticsExam.com

15. Que.: 5 - Which statement is true regarding decision trees and models based on ensembles of trees? Options: a) In the gradient boosting algorithm, for all but the first iteration, the target is the residual from the previous decision tree model. b) For a Forest model, the out-of-bag sample is simply the original validation data set from when the raw data partitioning took place. c) In the Forest algorithm, each individual tree is pruned based on using minimum Average Squared Error. d) A single decision tree will always be outperformed by a model based on an ensemble of trees. Rise & Shine with AnalyticsExam.com

16. Answer: a) In the gradient boosting algorithm, for all but the first iteration, the target is the residual from the previous decision tree model. Rise & Shine with AnalyticsExam.com

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