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IBM C2020-010 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Model Building | - Predictive Modeling
|
| Deployment and Streams | - SPSS Modeler Streams
|
| Data Preparation | - Data Integration
|
| Model Evaluation | - Model Comparison
|
| Data Understanding | - Data Exploration
|
IBM SPSS Modeler Professional v2 Sample Questions:
To score new data using an existing model, the user must:
- A. Not change the data source and develop a new model for that data.
- B. Change the data source and rename the fields as needed to match the names of the fields in original file.
- C. Change the data source and develop a new model for that data.
- D. Change the data source and rename the fields so they do not match the names of the fields in original file.
Using the Linear node, what are the advantages of creating a standard model without boosting or bagging? (Choose two.)
- A. Easier interpretation
- B. Higher accuracy
- C. None of the above
- D. Easter to score
Which statements are true of the Feature Selection node? (Choose two.)
- A. When a Target field is not specified then it will create a generalized output.
- B. The Target fields can be either categorical or continuous measurement.
- C. The Input fields can only be continuous measurement.
- D. Input fields can be selected on the basis of importance.
What is the function of the Ensemble node?
- A. The development of additional fields to improve accuracy
- B. Transforming the existing values to improve accuracy
- C. None of the above
- D. Combining models together to produce higher accuracy
Assume you have four fields, 4, A, C and D and you wish to find the sum of the values even if there are $null$ missing values in one or more fields. Which expression would accomplish this task?
- A. to_real(A) + to_real(B) + to_real(C) + to_real(D)
- B. sum_n([A, B, C, D])
- C. Sum_n((ABCIJ))
- D. A+B+C+D
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