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

（A）The top bar represents the node with the highest count.

（B）The wider bars represent nodes with a higher probability of event.

（C）The darker bars represent nodes with a lower probability of event.

（D）The top bar represents the node with the highest probability of event.

（A）The process of feature selection

（B）Periodically updating and improving a deployed model with new data

（C）The process of building the initial model

（D）The process of data preprocessing

（A）To remove irrelevant features

（B）To visualize the data

（C）To prepare the data for analysis and modeling

（D）To train the model

（A）Tokenization

（B）Standardization

（C）One-Hot Encoding

（D）Principal Component Analysis (PCA)

（A）F1 score is applicable to a model with an interval target.

（B）F1 score is calculated based on a cut off value.

（C）F1 score is calculated based on a depth value.

（D）F1 score is applicable to a model with a binary target.

（A）The process of building a model

（B）The process of selecting features

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

（D）The process of data cleaning

（A）To learn from labeled data

（B）To maximize a cumulative reward over time

（C）To make predictions

（D）To generate synthetic data

（A）Autoencoder

（B）Principal component analysis

（C）Singular value decomposition

（D）Robust PCA

（A）Visualizing data

（B）Training the model

（C）Optimizing the model's hyperparameters for better performance

（D）Selecting the most important features

（A）MySQL

（B）Microsoft SQL Server

（C）Oracle Database

（D）MongoDB