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SASInstitute A00-406 Exam Syllabus Topics:

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

1. 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

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

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

3. When deploying a machine learning model, what is meant by "model latency"?

A) The time it takes to train a model
B) The time it takes for the model to make predictions once deployed
C) The time it takes to create synthetic data
D) The time it takes to build a model

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

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

5. What is feature engineering in the context of machine learning pipelines?

A) Applying the model to new data
B) Testing the model's performance
C) Building a machine learning model from scratch
D) Creating new features from existing data

Solutions: