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SASInstitute A00-406 Exam Syllabus Topics:

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

1. What is the purpose of cross-entropy loss in machine learning, especially in the context of classification?

A) To quantify the variance of a model
B) To evaluate feature importance
C) To measure the dissimilarity between predicted and actual class probabilities
D) To calculate the mean squared error of a regression model

2. Which evaluation metric is commonly used for assessing the performance of a binary classification model?

A) R-squared
B) Accuracy
C) Root Mean Squared Error (RMSE)
D) Mean Absolute Error (MAE)

3. What is the purpose of cross-validation in model building and evaluation?

A) Reducing the dataset size
B) Splitting the dataset into training and testing sets
C) Assessing the model's generalization performance
D) Generating synthetic data

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

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

5. Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?

A) Learning rate
B) Min samples split
C) Max features
D) Max depth

Solutions: