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SASInstitute A00-406 Exam Syllabus Topics:

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

1. In a machine learning pipeline, what is the purpose of cross-validation?

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

2. Which of the following best describes unstructured data?

A) Data that is difficult to process and lacks a predefined structure
B) Data that is organized in rows and columns
C) Data with a clear schema
D) Data stored in a relational database

3. What is the primary objective of model evaluation in the context of building predictive models?

A) Discovering patterns in data
B) Visualizing data
C) Cleaning the data
D) Assessing the model's performance and accuracy

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

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

5. What is the primary goal of building models in data science and machine learning?

A) Data cleaning
B) Feature engineering
C) Data visualization
D) Making predictions or inferences from data

Solutions: