How to Prepare for SAS Machine Learning A00 - 402 Certification? A00 - 402 Certification Made Easy with AnalyticsExam.com SAS A00 - 402 Exam Summary: Exam Name SAS Certified Specialist - Machine Learning Using SAS Viya 3.5 Exam Code A00 - 402 No. of Questions 50 - 55 Passing Score 65% Time Limit 100 minutes Exam Fees $180 (USD) Online Practice Test SAS Machine Learning Certification Practice Exam Sample Questions SAS Machine Learning Certification Sample Question Rise & Shine with AnalyticsExam.com SAS Machine Learning Syllabus Content: Syllabus Topics: ● Data Sources (30%) ● Building Models (50%) ● Model Assessment and Deployment (20%) Rise & Shine with AnalyticsExam.com SAS Machine Learning Training: Recommended Training: ● Machine Learning Using SAS Viya Rise & Shine with AnalyticsExam.com SAS Machine Learning Book: Recommended Book: ● Machine Learning with SAS® Viya Rise & Shine with AnalyticsExam.com Tips to Prepare for A00 - 402 ● Understand the all Syllabus Topics. ● Perform SAS Machine Learning online test at AnalyticsExam.com ● Identify your weak areas from SAS Machine Learning mock test and asses yourself frequently. Rise & Shine with AnalyticsExam.com SAS A00 - 402 Sample Questions Rise & Shine with AnalyticsExam.com Options: a) Advisor Options for missing values b) Rules for model comparison statistic c) Partition Data percentages d) Event - based Sampling proportions Rise & Shine with AnalyticsExam.com Que.: 1 : A project has been created and a pipeline has been run in Model Studio. Which project setting can you edit? Answer: Rise & Shine with AnalyticsExam.com b) Rules for model comparison statistic Que.: 2 : Which feature extraction method can take both interval variables and class variables as inputs? Options: a) Principal component analysis b) Autoencoder c) Singular value decomposition d) Robust PCA Rise & Shine with AnalyticsExam.com Answer: Rise & Shine with AnalyticsExam.com b) Autoencoder Que.: 3 : In Model Studio, you have multiple pipelines in a project. Which statement is true? Options: a) The Model Comparison node compares only the champion models for each project. b) The Pipeline Comparison tab compares all of the models from each pipeline. c) You can override the champion in a Model Comparison node. d) You can override the champion in a Pipeline Comparison tab. Rise & Shine with AnalyticsExam.com Answer: Rise & Shine with AnalyticsExam.com d) You can override the champion in a Pipeline Comparison tab. Que.: 4 : Which statements are true for the F1 score? (Choose 2.) Options: a) F1 score is calculated based on a depth value b) F1 score is calculated based on a cut off value c) F1 score is applicable to a model with a binary target. d) F1 score is applicable to a model with an interval target. Rise & Shine with AnalyticsExam.com Answer: Rise & Shine with AnalyticsExam.com b) F1 score is calculated based on a cut off value c) F1 score is applicable to a model with a binary target. Que.: 5 : As the number of input variables in a problem increases, there is an exponential increase in the number of observations needed to densely populate the feature space. This is referred to as: Options: a) Problem of rare events b) Multicollinearity c) Curse of Dimensionality d) Underfitting Rise & Shine with AnalyticsExam.com Answer: Rise & Shine with AnalyticsExam.com c) Curse of Dimensionality Follow Us on: Rise & Shine with AnalyticsExam.com