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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Business Understanding and Planning | 10% | - CRISP-DM methodology - Align business objectives to data mining goals |
| Topic 2: SPSS Modeler Professional Functionality | 10% | - Stream creation and management - Node palettes and SuperNodes - Scripting and automation |
| Topic 3: Modeling | 20% | - Association and sequence models - Clustering and segmentation models - Classification and regression models - Auto-modeling and ensemble techniques |
| Topic 4: Data Preparation | 20% | - Data integration, merging and appending - Sampling, partitioning and balancing - Data transformation and cleaning - Filtering, aggregation and refinement |
| Topic 5: Evaluation and Analysis | 15% | - Interpretation and visualization of results - Validation methods - Model performance assessment |
| Topic 6: Deployment | 10% | - Scoring new data - Stream export and integration - Reporting and documentation |
| Topic 7: Data Understanding | 15% | - Identify data quality issues - Explore and visualize data |
1. The Distribution node produces which output? (Select all that apply)
A) Chart showing the distribution for a continuous field
B) Table displaying the percentage and count for each category of a field
C) Summary statistics for a continuous field
D) Chart showing the count for each category of 3 field
2. Examine the Matrix table shown below which relates the field churn (whether a customer will churn) to age cat (age groups).
Based on the table, which statements are accurate? (Select all that apply)
A) 1.768% of those that churn are over 65 years of age.
B) Measured by Chi-square is a weak relationship.
C) Older age groups are less likely to churn than the younger ages.
D) 42.283% of the 18-24 year old will churn.
3. The Anomaly node is on which parameter or menu group?
A) Record Ops
B) Output
C) Field Ops
D) Modeling
4. What are the options for defining outliers in the Data Audit node? (Select all that apply)
A) Standard deviation from the mean
B) Values that represent less than 15% of all values
C) Eigen values greater than 1
D) Interlunar the number
5. Assume you have two data files: a personnel file with information about employees and a second file with information about performance categories. You will use IBM SPSS Modeler to merge the files together using performance category as the key. However, there are inconsistence in the category keys used in both in the personnel file and the performance file. In order to merge the files together without losing information from either the personnel file or the performance category file, you would use:
A) A full outer join
B) An inner join
C) A partial outer join
D) An anti-join
Solutions:
| Question # 1 Answer: A,B | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: A,D | Question # 5 Answer: A |
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