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SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions (Q58-Q63):

NEW QUESTION # 58
What does "feature selection" refer to in the context of model building?

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

Answer: C

NEW QUESTION # 59
Which algorithm is commonly used for binary classification in machine learning pipelines, especially when dealing with imbalanced datasets?

A. Support Vector Machine (SVM)
B. K-Means Clustering
C. Principal Component Analysis (PCA)
D. Linear Regression

Answer: A

NEW QUESTION # 60
Which of the following is a common technique for handling missing data in a machine learning pipeline?

A. Imputing missing values
B. Deleting rows with missing data
C. Ignoring missing data
D. Replacing missing values with zeros

Answer: A

NEW QUESTION # 61
In a machine learning pipeline, what is the purpose of cross-validation?

A. To evaluate the model's performance on new data
B. To train multiple models on different subsets of the data to assess generalization
C. To visualize the data distribution
D. To split the dataset into training and testing sets

Answer: B

NEW QUESTION # 62
Which algorithm is commonly used for decision-making tasks in classification models?

A. Principal Component Analysis (PCA)
B. K-Means
C. Linear Regression
D. Decision Trees

Answer: D

NEW QUESTION # 63
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