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| Section | Weight | Objectives |
|---|---|---|
| Business Understanding and Planning | 10% | - CRISP-DM methodology - Align business objectives to data mining goals |
| Data Understanding | 15% | - Identify data quality issues - Explore and visualize data |
| Data Preparation | 20% | - Filtering, aggregation and refinement - Data integration, merging and appending - Sampling, partitioning and balancing - Data transformation and cleaning |
| Modeling | 20% | - Classification and regression models - Association and sequence models - Auto-modeling and ensemble techniques - Clustering and segmentation models |
| SPSS Modeler Professional Functionality | 10% | - Node palettes and SuperNodes - Stream creation and management - Scripting and automation |
| Deployment | 10% | - Stream export and integration - Scoring new data - Reporting and documentation |
| Evaluation and Analysis | 15% | - Model performance assessment - Interpretation and visualization of results - Validation methods |
1. The Feature Selection node is used for what reasons? (Select all that apply)
A) Screens input fields for problems such as missing data.
B) Ranks the model by importance.
C) Selects the best fitting model
D) Selects the best input fields for model.
2. Which statement accurately describes the function of the Coerce data check setting Type node?
A) Checks fully instantiated fields for values outside the specified range and reports unspecified values.
B) Checks fully instantiated fields for values outside the specified range and converts unspecified values to legal values.
C) Checks all fields for values outside the specified range and reports unspecified values.
D) None of the above is accurate.
3. Which node is best suited for revealing relationships between continuous and categorical (nominal, ordinal) fields?
A) Means node
B) Distribution node
C) Statistics node
D) Table node
4. Which nodes can be used to replace missing data with valid values? [Select all that apply.)
A) Data Audit node
B) Distribution node
C) Filler node
D) Filter node
5. Examine the output generated by the K-Means clustering model.
Study the results of distribution node relating the clusters created by K-means to the outcome (dependant) field called churned (current=current customers; Vol = customers that left inVol = customers that were dropped because they did not pay)
If the goal is to not lose the InVol customers, what can be done to reduce the sketched of this occurring?
A) Improve the long distance calling plan and rates.
B) Improve the international calling plan and rates
C) Improve the local calling plan and rates
D) Create a "Pay as You Go" plan (pay only for the minutes you use)
Solutions:
| Question # 1 Answer: A,B | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: A,C,D | Question # 5 Answer: D |
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