Last Updated: Aug 20, 2026
No. of Questions: 98 Questions & Answers with Testing Engine
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| Section | Weight | Objectives |
|---|---|---|
| Building Models | 40-46% | - Use model interpretability tools - Build decision trees and ensemble models - Build regression models - Understand supervised machine learning concepts - Integrate custom code - Build neural networks and SVM models |
| Model Assessment and Deployment | 24-30% | - Apply model assessment principles - Select champion models - Evaluate and compare model performance - Deploy models into production |
| Data Sources | 30-36% | - Perform variable selection - Reduce dimensionality - Modify and prepare data - Create a project in Model Studio - Explore and understand data |
1. When building a recommendation system, what does "collaborative filtering" rely on?
A) The popularity of items
B) Item-based clustering
C) The past behavior or preferences of users
D) The characteristics of the items being recommended
2. What is the primary objective of model validation during the model assessment phase?
A) To ensure the model generalizes well to new, unseen data
B) To create synthetic data
C) To assess the accuracy of the model
D) To build a model from scratch
3. Refer to the exhibit below:
Based on the output from the Data Exploration node shown in the exhibit, which variable has the most thin tails (most platykurtic distribution)?
A) Logi_rfm6
B) Logi_rfm8
C) Logi_rfm4
D) Logi_rfm12
4. What is the main purpose of feature engineering in model building?
A) Data preprocessing
B) Model evaluation
C) Data visualization
D) Creating new features or transforming existing ones to improve model performance
5. What does API stand for in the context of data sources?
A) Application Programming Interface
B) Advanced Programming Integration
C) Application Program Interface
D) Automated Program Integration
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: A |
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