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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building Models | 40-46% | - Understand supervised machine learning concepts - Build regression models - Integrate custom code - Build decision trees and ensemble models - Build neural networks and SVM models - Use model interpretability tools |
| Topic 2: Model Assessment and Deployment | 24-30% | - Apply model assessment principles - Evaluate and compare model performance - Select champion models - Deploy models into production |
| Topic 3: Data Sources | 30-36% | - Reduce dimensionality - Perform variable selection - Create a project in Model Studio - Explore and understand data - Modify and prepare data |
1. Which of the following is a common technique for handling missing data in a machine learning pipeline?
A) Deleting rows with missing data
B) Replacing missing values with zeros
C) Ignoring missing data
D) Imputing missing values
2. What is the primary purpose of "continuous integration and continuous deployment" (CI/CD) in the context of model deployment?
A) To automate the testing, integration, and deployment of new model versions
B) To visualize data distribution
C) To create synthetic data
D) To evaluate the model's accuracy
3. What is the primary purpose of model deployment in the context of data science and machine learning?
A) Data preprocessing
B) Model evaluation
C) Model building
D) Making the model available for use in real-world applications
4. In model assessment, what does "cross-validation" aim to address?
A) Data preprocessing
B) Model deployment
C) Overfitting and generalization
D) Training a model
5. 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 measure feature importance
D) To compare a model's true positive rate with the false positive rate
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: D |
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