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SASInstitute A00-406 Exam Syllabus Topics:

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

1. What is metadata in the context of data sources?

A) Data that is in a non-standard, proprietary format
B) Data that is stored in a physical format
C) Data that is encrypted for security
D) Data about data, providing information such as data source, structure, and context

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

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

3. Which of the following is a common technique for handling missing data in a machine learning pipeline?

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

4. In the context of data sources, what is ETL?

A) Efficient Text Link
B) Examine, Test, Log
C) Extract, Transform, Load
D) Execute, Terminate, Launch

5. Which statement is true regarding decision trees and models based on ensembles of trees?

A) In the gradient boosting algorithm, for all but the first iteration, the target is the residual from the previous decision tree model.
B) For a Forest model, the out-of-bag sample is simply the original validation data set from when the raw data partitioning took place.
C) In the Forest algorithm, each individual tree is pruned based on using minimum Average Squared Error.
D) A single decision tree will always be outperformed by a model based on an ensemble of trees.

Solutions: