Welcome to your one-stop solution for all the information you need to excel in the SAS Certified Associate - Applied Statistics for Machine Learning (A00-480) Certification exam. This page provides an in-depth overview of the SAS A00-480 Exam Summary, Syllabus Topics, and Sample Questions, designed to lay the foundation for your exam preparation. We aim to help you achieve your SAS Certified Associate - Applied Statistics for Machine Learning certification goals seamlessly. Our detailed syllabus outlines each topic covered in the exam, ensuring you focus on the areas that matter most. With our sample questions and practice exams, you can gauge your readiness and boost your confidence to take on the SAS Applied Statistics for Machine Learning exam.
Why SAS Applied Statistics for Machine Learning Certification Matters
The SAS A00-480 exam is globally recognized for validating your knowledge and skills. With the SAS Certified Associate - Applied Statistics for Machine Learning credential, you stand out in a competitive job market and demonstrate your expertise to make significant contributions within your organization. The SAS Certified Associate - Applied Statistics for Machine Learning Certification exam will test your proficiency in the various syllabus topics.
SAS A00-480 Exam Summary:
| Exam Name | SAS Certified Associate - Applied Statistics for Machine Learning |
| Exam Code | A00-480 |
| Exam Duration | 105 minutes |
| Exam Questions | 60 |
| Passing Score | 68% |
| Exam Price | $120 (USD) |
| Books / Training | Statistics You Need to Know for Machine Learning |
| Exam Registration | Pearson VUE |
| Sample Questions | SAS Applied Statistics for Machine Learning Certification Sample Question |
| Practice Exam | SAS Applied Statistics for Machine Learning Certification Practice Exam |
SAS A00-480 Exam Syllabus Topics:
| Objective | Details |
|---|---|
Statistics and Machine Learning (9 – 12%) |
|
| Relevance of Statistics in Big Data and Machine Learning |
- Describe ways of obtaining data, the different types of data, and how each type of data is analyzed - Define big data and identify smart applications produced by it - Compare and contrast of machine learning and classical statistics - Explain the importance of statistics in machine learning |
| Terminology and Vocabulary |
- Relate statistical terminology with machine learning - Compare variable types and level of measurements - Explore common modeling vocabulary |
Fundamental Statistical Concepts (17 – 21%) |
|
| Basics of Statistical Analysis |
- Distinguish between populations and samples - Describe the process of statistical analysis - Compare and contrast inferential and descriptive statistics - Explain different methods of sampling data including event-based sampling |
| Descriptive Statistics |
- Define measures of central tendency, position, and dispersion - Explain how to visualize a distribution with different graphics - Describe usefulness of the normal distribution in machine learning - Explain measures of distribution shape |
| Inferential Statistics |
- Explain sampling distributions and how to make inferences from data - Explain confidence intervals and hypothesis tests - Define a one-sample t Test - Describe usefulness of p-values in machine learning |
| Explanatory Modeling Using Linear Regression (18 – 24%) | |
| Correlation and Simple Linear Regression |
- Define explanatory modeling - Explore bivariate relationships using scatterplots - Compare and contrast correlation and covariance - Identify irrelevant and redundant predictors using correlation - Explain simple linear regression and OLS estimation - Test regression hypothesis and assess model fit |
| Multiple Regression and Model Selection |
- Define multiple linear regression - Use categorical predictors - Define ANOVA and relate it with regression - Explain interaction effects - Compare regression models using R-square, Adjusted R-square, and Information Criteria - Describe sequential model selection methods |
| Model Diagnostics |
- Define the assumptions of linear regression - Verify assumptions with Residual Plots - Diagnose and remedy collinearity - Explain problems with outliers, leverage points, and influential observations - Diagnose influential and outlier cases |
Predictive Modeling Using Logistic Regression (25 – 31%) |
|
| Introduction to Predictive Modeling |
- Compare and contrast explanatory and predictive modeling - Describe predictive modeling concepts - Define honest assessment including data partitioning - Explain how to incorporate different time frames for predictive modeling - Explain how to optimize model complexity for prediction - Explain the bias-variance tradeoff |
| Categorical Associations |
- Explain association between categorical predictors - Define and use Cramer's V statistic - Explain and interpret odds ratios |
| Logistic Regression Model |
- Define logistic regression - Define odds and log odds - Describe maximum likelihood estimation - Interpret logistic regression coefficients - Assess logistic regression model fit - Use categorical predictors - Explain interaction effects - Compare logistic regression models using concordant/discordant pairs, c-statistic, and Information Criteria - Describe sequential model selection methods |
| Model Deployment |
- Explain how to deploy a logistic regression model - Describe scoring a logistic regression model - Explain the classification cutoff for scoring |
Statistical Foundations of Machine Learning (18 – 24%) |
|
| Overview of Machine Learning |
- Define machine learning - Define supervised, unsupervised, semi-supervised, and reinforcement learning - Explain neural networks - Name common algorithms in machine learning - Distinguish between data preparation and data preprocessing |
| Data Pre-processing for Machine Learning Models |
- Describe common difficulties with modeling data for machine learning - Describe challenges in visualizing big data - Diagnose and correct problems with errors, missing values, and outliers - Explain why transform input variables and discuss some simple transformations - Diagnose problems with high dimensional data and feature engineering remedy - Discuss feature scaling |
| Model Evaluation, Estimation, and Post-training Tasks |
- Explain signal-noise dynamics - Define cross-validation and bootstrap aggregation - Explain coefficient shrinkage and why it can be useful - Define L1, L2 and L12 regularizations - Explain learning process and estimation criteria in machine learning - Differentiate between parameters and hyperparameters - Explain model interpretability |
The SAS has created this credential to assess your knowledge and understanding in the specified areas through the A00-480 certification exam. The SAS Certified Associate - Applied Statistics for Machine Learning exam holds significant value in the market due to the brand reputation of SAS. We highly recommend thorough study and extensive practice to ensure you pass the SAS Certified Associate - Applied Statistics for Machine Learning exam with confidence.