SAS Institute A00-406 SAS Viya Supervised Machine Learning Pipelines Online Training
SAS Institute A00-406 Online Training
The questions for A00-406 were last updated at Nov 22,2024.
- Exam Code: A00-406
- Exam Name: SAS Viya Supervised Machine Learning Pipelines
- Certification Provider: SAS Institute
- Latest update: Nov 22,2024
What is the primary purpose of model assessment in the context of data science and machine learning?
- A . Data preprocessing
- B . Model building
- C . Evaluating and selecting the best-performing model
- D . Data visualization
What is the main advantage of ensemble learning methods, such as Random Forest, in a machine learning pipeline?
- A . They are simple and easy to interpret.
- B . They are not suitable for large datasets.
- C . They combine multiple models to improve predictive performance.
- D . They require minimal data preprocessing.
When deploying a machine learning model, what is meant by "model latency"?
- A . The time it takes to build a model
- B . The time it takes to train a model
- C . The time it takes for the model to make predictions once deployed
- D . The time it takes to create synthetic data
What is the purpose of an ROC curve (Receiver Operating Characteristic) in model assessment?
- A . To evaluate regression models
- B . To visualize data distribution
- C . To compare a model’s true positive rate with the false positive rate
- D . To measure feature importance
Which data source typically provides access to real-time financial market data?
- A . Social media platforms
- B . Weather stations
- C . Stock market APIs
- D . Online news websites
In model assessment, what does "cross-validation" aim to address?
- A . Training a model
- B . Overfitting and generalization
- C . Data preprocessing
- D . Model deployment
Which metric is commonly used to evaluate the performance of a regression model?
- A . F1 Score
- B . Mean Absolute Error (MAE)
- C . Precision
- D . Confusion Matrix
What is "model reevaluation" in the model deployment phase?
- A . The process of data preprocessing
- B . The process of selecting features
- C . The periodic assessment of a deployed model’s performance and potential retraining
- D . The evaluation of data distribution
What is overfitting in machine learning, and how can it be addressed in a pipeline?
- A . Overfitting occurs when the model is too simple and underperforms.
- B . Overfitting occurs when the model fits the training data too closely and may not generalize well. It can be addressed by regularization techniques.
- C . Overfitting occurs when the model is too complex and overperforms.
- D . Overfitting is not a concern in machine learning pipelines.
What is a data lake?
- A . A data storage solution designed for high-speed data retrieval
- B . A centralized repository for storing all structured and unstructured data at any scale
- C . A specialized database for time-series data
- D . A backup system for relational databases
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