SASInstitute A00-406

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

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

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

2. Which type of model is well-suited for solving classification problems when dealing with high- dimensional data, such as text?

A) K-Means Clustering
B) Linear Regression
C) Random Forest
D) Support Vector Machine (SVM)

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

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

4. Which metric is commonly used to evaluate the performance of a regression model?

A) Precision
B) F1 Score
C) Confusion Matrix
D) Mean Absolute Error (MAE)

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

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

Solutions: