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

（A）Standardization

（B）One-Hot Encoding

（C）Decision Trees

（D）Principal Component Analysis (PCA)

（A）Random Forest

（B）K-Means Clustering

（C）Support Vector Machine (SVM)

（D）Linear Regression

（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）Confusion Matrix

（B）Mean Absolute Error (MAE)

（C）F1 Score

（D）Precision

（A）Internal emails

（B）Customer surveys

（C）Employee databases

（D）Intranet portals

（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）To train the machine learning model

（B）To test the model's generalization capability

（C）To validate the model's performance

（D）To evaluate the model's predictions

（A）Model building

（B）Data preprocessing

（C）Data visualization

（D）Evaluating and selecting the best-performing model

（A）Model building

（B）Data preprocessing

（C）Making the model available for use in real-world applications

（D）Model evaluation

（A）Backing up data for disaster recovery

（B）Extracting data from source systems

（C）Storing data in a centralized repository

（D）Converting and reshaping data to match the target schema