SASInstitute SAS® Viya® Supervised Machine Learning Pipelines : A00-406

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SASInstitute A00-406 Exam Syllabus Topics:

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

1. When deploying a model, what is "model explainability"?

A) The process of data preprocessing
B) The simplicity of the model
C) The time it takes to make predictions
D) The capability to interpret and understand the model's decisions and predictions

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

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

3. In the context of data integration, what does "data transformation" refer to?

A) Extracting data from source systems
B) Converting and reshaping data to match the target schema
C) Backing up data for disaster recovery
D) Storing data in a centralized repository

4. 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

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

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

Solutions: