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A00-480 Practice Test A00-480 is SAS Certified Associate - Applied Statistics for Machine Learning– Certification offered by the SAS. Since you want to comprehend the A00-480 Question Bank, I am assuming you are already in the manner of preparation for your A00-480 Certification Exam. To prepare for the actual exam, all you need is to study the content of this exam questions. You can recognize the weak area with our premium A00-480 practice exams and help you to provide more focus on each syllabus topic covered. This method will help you to increase your confidence to pass the SAS Applied Statistics for Machine Learning certification with a better score.
SAS Certified Associate - Applied Statistics for Machine Learning
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A00-480 Exam Details 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 Certified Associate - Applied Statistics for Machine Learning
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A00-480 Exam Syllabus 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 - Distinguish between populations and samples Analysis - Describe the process of statistical analysis - Compare and contrast inferential and descriptive statistics - Explain different methods of sampling data including eventbased 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
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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 - Define multiple linear regression and Model - Use categorical predictors Selection - 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 - Compare and contrast explanatory and predictive modeling 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
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Categorical Associations
- Explain association between categorical predictors - Define and use Cramer's V statistic - Explain and interpret odds ratios
Logistic Regression - Define logistic regression Model - 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 Preprocessing 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
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- Diagnose problems with high dimensional data and feature engineering remedy - Discuss feature scaling Model Evaluation, - Explain signal-noise dynamics Estimation, and - Define cross-validation and bootstrap aggregation Post-training Tasks - 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
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A00-480 Questions and Answers Set 01. You built a logistic regression model to predict if a financial transaction is fraudulent (fraud = 1) or not. Given the values of inputs, you obtained a predicted probability of 0.20. What's the meaning of this predicted probability? (Choose 2.) a) There is a 80% chance that the transaction is fraudulent. b) There is a 20% chance that the transaction is fraudulent. c) There is a 80% chance that the transaction is non-fraudulent. d) There is a 20% chance that the transaction is non-fraudulent. Answer: b, c
02. What types of input and target can be used for logistic regression? a) A continuous target and an ordinal input. b) An interval target and an ordinal input. c) An ordinal target and an interval input. d) A ratio target and a nominal input. Answer: c
03. What statistics are commonly used for selecting variables during a sequential model selection process? (Choose 2.) a) F-statistic b) Bayesian Information Criterion (BIC) c) Significance Level d) Mean Absolute Error (MAE) Answer: b, c
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04. Which of the following distinguishes between model parameters and hyperparameters? a) Model parameters govern the learning process and computational aspects, while hyperparameters primarily influence the model's predictions. b) The values of model parameters can significantly influence the selection of optimal hyperparameters, while hyperparameters do not directly impact the values of model parameters. c) Model parameters and hyperparameters are used interchangeably when referring to the factors affecting the performance of a model. d) Model parameters can be learned by the model through the training process, while hyperparameters must be set before training of the model or autotuned using optimization algorithms. Answer: d
05. What is the range of Cramer's V statistic? a) 0 to 1 b) -1 to 1 c) 0 to ∞ d) -∞ to ∞ Answer: a
06. What does a confidence interval of 95% imply? a) 95% of repeated samples will produce intervals containing the population parameter. b) 95% of the sample data will fall within the interval. c) There is a 95% probability that the interval contains the population parameter. d) The population mean will always lie within the interval. Answer: a
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07. Which evaluation metric is most appropriate for a classification model with imbalanced classes? a) Accuracy b) Precision c) Recall d) F1 Score Answer: d
08. Which algorithm uses trial and error to discover which action yields the greatest rewards? a) Supervised Learning b) Unsupervised Learning c) Semi-supervised Learning d) Reinforcement Learning Answer: d
09. You are gathering data on products purchased by customers. What is the level of measurement of the brand of the product? a) Ratio b) Interval c) Ordinal d) Nominal Answer: d
10. You are gathering data on the circumference of tree trunks in centimeters. What is the level of measurement for circumference? a) Interval b) Ratio c) Ordinal d) Nominal Answer: b
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