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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation | 30% | - Automated data preparation - Deriving and reclassifying variables - Sampling, aggregating and cleaning data - Combining and restructuring data - General operations in SPSS Modeler |
| Topic 2: Evaluation and Deployment | 15% | - Model assessment - Interpreting and exporting results |
| Topic 3: Business Understanding | 15% | |
| Topic 4: Data Understanding | 20% | |
| Topic 5: Modeling | 20% | - Applying data mining techniques - CRISP-DM methodology |
1. An online retailer wants to identify groups of customers based on components of their buying behavior, such as types of products purchased, volume of purchases, and frequency of purchases. What type of model would be used?
A) Segmentation model
B) Sequence model
C) Association model
D) Classification model
2. Which node can be used to easily generate a field that divides the data into subsamples for the Training and Testing stages of modeling?
A) Balance
B) Partition
C) Filler
D) Sample
3. Which method would be used on the Merge node in order to combine a file containing 100 products and a file containing 50 suppliers and retain only the matching records?
A) Inner join
B) Partial outer join
C) Full outer join
D) Anti-join
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: A |
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