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

（A）Min samples split

（B）Max depth

（C）Max features

（D）Learning rate

（A）The overall accuracy of a model

（B）Systematic errors that cause a model to consistently underpredict or overpredict

（C）The simplicity of a model

（D）A model's inability to generalize to new data

（A）To assess data quality

（B）To evaluate the model's accuracy

（C）To create synthetic data

（D）To compare two different versions of a model or strategy to determine which performs better

（A）To select the most important features

（B）To optimize the model's hyperparameters for better performance

（C）To train the model

（D）To evaluate the model's predictions

（A）To introduce non-linearity

（B）To initialize the model

（C）To control the number of hidden layers

（D）To define the learning rate

（A）Data that is stored in a physical format

（B）Data about data, providing information such as data source, structure, and context

（C）Data that is in a non-standard, proprietary format

（D）Data that is encrypted for security

（A）The evaluation of data distribution

（B）The process of selecting features

（C）The process of data preprocessing

（D）The periodic assessment of a deployed model's performance and potential retraining

（A）The time it takes to create synthetic data

（B）The time it takes to build a model

（C）The time it takes for the model to make predictions once deployed

（D）The time it takes to train a model

（A）Data cleaning

（B）Feature engineering

（C）Data visualization

（D）Making predictions or inferences from data

（A）A backup system for relational databases

（B）A specialized database for time-series data

（C）A data storage solution designed for high-speed data retrieval

（D）A centralized repository for storing all structured and unstructured data at any scale