SASInstitute SAS® Viya® Supervised Machine Learning Pipelines : A00-406

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SASInstitute A00-406 Exam Syllabus Topics:

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

1. What does "feature selection" refer to in the context of model building?

A) The visualization of data distribution
B) The creation of synthetic features from existing data
C) The evaluation of model accuracy
D) The process of choosing the most relevant variables (features) for the model

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

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

3. Which technique is used for feature selection in a machine learning pipeline when dealing with a large number of features?

A) Principal Component Analysis (PCA)
B) Naive Bayes
C) One-Hot Encoding
D) Regularization

4. In the context of model deployment, what is "model compliance"?

A) The process of feature selection
B) The degree to which the model adheres to regulatory or ethical guidelines
C) The model's efficiency
D) The model's simplicity

5. In model evaluation, what is the purpose of a ROC curve (Receiver Operating Characteristic)?

A) To measure feature importance
B) To evaluate the mean squared error of a model
C) To compare models' performance in terms of sensitivity and specificity
D) To visualize data distribution

Solutions: