최신 A00-406 무료덤프 - SASInstitute SAS® Viya® Supervised Machine Learning Pipelines

A. Data analysis

B. Data documentation and discovery

C. Data visualization

D. Data storage

A. Confusion Matrix

B. F1 Score

C. Precision

D. Mean Absolute Error (MAE)

A. The time it takes to create synthetic data

B. The time it takes to build a model

C. The time it takes to train a model

D. The time it takes for the model to make predictions once deployed

A. Cleaning the data

B. Discovering patterns in data

C. Assessing the model's performance and accuracy

D. Visualizing data

A. To compute the mean squared error

B. To evaluate model performance, especially for binary classification

C. To visualize the data

D. To summarize the distribution of target variables

A. Collaborative filtering

B. Matrix factorization

C. Content-based filtering

D. Singular Value Decomposition (SVD)

A. To optimize data storage

B. To assess the quality and characteristics of data

C. To execute data queries

D. To create data visualizations

A. HTML (Hypertext Markup Language)

B. SQL (Structured Query Language)

C. JSON (JavaScript Object Notation)

D. CSV (Comma-Separated Values)

A. A measure of the model's precision

B. The process of feature engineering

C. A systematic error that causes a model to consistently overpredict

D. The simplicity of the model

A. They work well with high-dimensional data

B. They combine multiple models to improve predictive performance

C. They require minimal data preprocessing

D. They produce simple and interpretable models