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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building Models | 40-46% | - Build decision trees and ensemble models - Understand supervised machine learning concepts - Use model interpretability tools - Build neural networks and SVM models - Integrate custom code - Build regression models |
| Topic 2: Model Assessment and Deployment | 24-30% | - Apply model assessment principles - Select champion models - Evaluate and compare model performance - Deploy models into production |
| Topic 3: Data Sources | 30-36% | - Explore and understand data - Reduce dimensionality - Create a project in Model Studio - Perform variable selection - Modify and prepare data |
Given the following properties for a neural network model, which statement is true regrading hidden units in the model? The following SAS program is submitted:
Correct Answer: D 🗳️
Which technique is used for feature selection in a machine learning pipeline when dealing with a large number of features?
Correct Answer: B 🗳️
Which evaluation metric is commonly used for assessing the performance of a binary classification model?
Correct Answer: A 🗳️
Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?
Correct Answer: C 🗳️
What is the primary difference between supervised and unsupervised learning in model building?
Correct Answer: B 🗳️
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