A00-406 無料問題集「SASInstitute SAS® Viya® Supervised Machine Learning Pipelines」

（A）To visualize the data

（B）To measure the difference between predicted and actual values

（C）To assess the accuracy of the model

（D）To maximize the model's performance

（A）Data stored in a relational database

（B）Data that is difficult to process and lacks a predefined structure

（C）Data with a clear schema

（D）Data that is organized in rows and columns

（A）Data storage and retrieval

（B）Data preprocessing and cleaning

（C）Data visualization

（D）Model training and evaluation

（A）Tokenization

（B）Standardization

（C）One-Hot Encoding

（D）Principal Component Analysis (PCA)

（A）To assess data quality

（B）To create synthetic features

（C）To evaluate the significance of input features in making predictions

（D）To visualize data distribution

（A）Mean Absolute Error (MAE)

（B）R-squared

（C）Root Mean Squared Error (RMSE)

（D）Accuracy

（A）The model's simplicity

（B）The process of feature selection

（C）The degree to which the model adheres to regulatory or ethical guidelines

（D）The model's efficiency

（A）A form of dimensionality reduction

（B）A technique that reduces model complexity

（C）The process of combining multiple identical models to reduce variance

（D）A type of feature extraction

（A）The accuracy of the model's predictions

（B）A numerical value that indicates the performance of an action taken by the agent

（C）A regularization parameter

（D）The final prediction made by the model

（A）Data storage

（B）Data analysis

（C）Data documentation and discovery

（D）Data visualization