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SASInstitute A00-406 Exam Syllabus Topics:

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

1. Which technique is commonly used for feature scaling or normalization in machine learning pipelines?

A) Decision Trees
B) Principal Component Analysis (PCA)
C) One-Hot Encoding
D) Standardization

2. When deploying a machine learning model, what is "model drift"?

A) A change in the distribution of the input data or target variable over time
B) The process of feature extraction
C) A measure of feature importance
D) A sudden increase in the model's accuracy

3. What is the purpose of hyperparameter tuning in a machine learning pipeline?

A) To optimize the model's hyperparameters for better performance
B) To train the model
C) To evaluate the model's predictions
D) To select the most important features

4. Which of the following is NOT a common data format for exchanging data between systems?

A) HTML (Hypertext Markup Language)
B) JSON (JavaScript Object Notation)
C) SQL (Structured Query Language)
D) CSV (Comma-Separated Values)

5. Which type of model is typically used for time-series forecasting?

A) Decision Trees
B) Logistic Regression
C) K-Means Clustering
D) AutoRegressive Integrated Moving Average (ARIMA)

Solutions: