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| Section | Weight | Objectives |
|---|---|---|
| Model Assessment and Deployment | 24-30% | - Select champion models - Apply model assessment principles - Evaluate and compare model performance - Deploy models into production |
| Building Models | 40-46% | - Build neural networks and SVM models - Understand supervised machine learning concepts - Integrate custom code - Build regression models - Build decision trees and ensemble models - Use model interpretability tools |
| Data Sources | 30-36% | - Modify and prepare data - Reduce dimensionality - Perform variable selection - Explore and understand data - Create a project in Model Studio |
1. What is the primary purpose of model deployment in the context of data science and machine learning?
A) Model evaluation
B) Data preprocessing
C) Model building
D) Making the model available for use in real-world applications
2. Refer to the treemap shown in the exhibit below:
Which statement is true about the tree map for a decision tree with a binary target?
A) The top bar represents the node with the highest probability of event.
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 count.
3. Which evaluation metric is commonly used for assessing the performance of a regression model?
A) Mean Absolute Error (MAE)
B) F1 Score
C) Confusion Matrix
D) Precision
4. Which data source allows for real-time data streaming and processing?
A) Cloud storage
B) Static data files
C) Data warehouses
D) IoT devices
5. In the context of data sources, what is meant by data versioning?
A) Keeping track of different versions or changes to data over time
B) Encrypting data to protect against unauthorized access
C) Storing multiple copies of the same data to increase redundancy
D) Compressing data to reduce storage space
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: A |
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