Last Updated: Aug 14, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Assessment and Deployment | 24-30% | - Select champion models - Deploy models into production - Evaluate and compare model performance - Apply model assessment principles |
| Topic 2: Data Sources | 30-36% | - Create a project in Model Studio - Modify and prepare data - Perform variable selection - Explore and understand data - Reduce dimensionality |
| Topic 3: Building Models | 40-46% | - Build neural networks and SVM models - Integrate custom code - Build regression models - Use model interpretability tools - Understand supervised machine learning concepts - Build decision trees and ensemble models |
1. Which machine learning technique is typically used for building a model to predict a numeric target variable?
A) Clustering
B) Dimensionality reduction
C) Classification
D) Regression
2. Which of the following is a common source for external data in the context of business analytics?
A) CRM data
B) Intranet databases
C) Company financial reports
D) Employee records
3. In the context of data sources, what is meant by data versioning?
A) Compressing data to reduce storage space
B) Storing multiple copies of the same data to increase redundancy
C) Keeping track of different versions or changes to data over time
D) Encrypting data to protect against unauthorized access
4. What is the primary role of a loss function in model training?
A) To maximize the model's performance
B) To assess the accuracy of the model
C) To visualize the data
D) To measure the difference between predicted and actual values
5. Which type of data source typically stores structured data in a tabular format?
A) APIs
B) NoSQL databases
C) Relational databases
D) Text documents
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: C |
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