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SASInstitute A00-406 Exam Syllabus Topics:

Section	Objectives
Topic 1: Supervised Machine Learning Models	- Regression and classification models 1. Tree-based models 2. Linear and logistic regression - Model selection techniques 1. Cross-validation 2. Performance comparison
Topic 2: Machine Learning Pipelines in SAS Viya	- Pipeline construction 1. Node-based pipeline design 2. Model Studio workflows - Model tuning and optimization 1. Automated machine learning (AutoML) 2. Hyperparameter tuning
Topic 3: Model Evaluation and Deployment	- Model deployment 1. Model governance 2. Scoring models in SAS Viya - Model assessment metrics 1. Accuracy, precision, recall 2. ROC and lift charts
Topic 4: Data Preparation for Machine Learning	- Feature engineering 1. Variable transformation 2. Encoding categorical variables - Data cleaning and preprocessing 1. Outlier detection and treatment 2. Handling missing values

SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:

1. What does API stand for in the context of data sources?

A) Automated Program Integration
B) Application Program Interface
C) Advanced Programming Integration
D) Application Programming Interface

2. In the context of model assessment, what does "bias" refer to?

A) The simplicity of the model
B) The process of feature engineering
C) A measure of the model's precision
D) A systematic error that causes a model to consistently overpredict

3. In model assessment, what is the purpose of feature importance analysis?

A) To visualize data distribution
B) To create synthetic features
C) To evaluate the significance of input features in making predictions
D) To assess data quality

4. When deploying a machine learning model, what is meant by "model latency"?

A) The time it takes to train a model
B) The time it takes for the model to make predictions once deployed
C) The time it takes to create synthetic data
D) The time it takes to build a model

5. When deploying a model, what is "model explainability"?

A) The time it takes to make predictions
B) The simplicity of the model
C) The capability to interpret and understand the model's decisions and predictions
D) The process of data preprocessing

Solutions:

Question # 1
Answer: D

Question # 2
Answer: D

Question # 3
Answer: C

Question # 4
Answer: B

Question # 5
Answer: C