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SASInstitute A00-406 Exam Syllabus Topics:

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

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

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

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

2. In the context of data sources, what is meant by data versioning?

A) Storing multiple copies of the same data to increase redundancy
B) Keeping track of different versions or changes to data over time
C) Compressing data to reduce storage space
D) Encrypting data to protect against unauthorized access

3. Which feature extraction method can take both interval variables and class variables as inputs?

A) Robust PCA
B) Singular value decomposition
C) Principal component analysis
D) Autoencoder

4. What is overfitting in machine learning, and how can it be addressed in a pipeline?

A) Overfitting occurs when the model is too complex and overperforms.
B) Overfitting is not a concern in machine learning pipelines.
C) Overfitting occurs when the model fits the training data too closely and may not generalize well. It can be addressed by regularization techniques.
D) Overfitting occurs when the model is too simple and underperforms.

5. Refer to the exhibit below:

Based on the output from the Data Exploration node shown in the exhibit, which variable has the most thin tails (most platykurtic distribution)?