IBM BAS-010 Exam Details & Actual Exam Questions
- Exam Code/Number: BAS-010
- Exam Name/Title: IBM SPSS Modeler Professional v2
- Certification Provider: IBM
- Corresponding Certification: IBM Certified Specialist
- Exam Questions: 55
- Updated On: Sep,09 2026
- Certification Level: Professional
IBM SPSS Modeler Professional v2 Exam Questions
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This exam has been stopped to register, new exam code replace: C2020-010
IBM BAS-010 Exam Overview:
| Certification Vendor: | IBM |
|---|---|
| Exam Name: | IBM SPSS Modeler Professional v2 |
| Exam Number: | BAS-010 |
| Related Certifications: | IBM Certified Professional - IBM SPSS Modeler |
| Exam Format: | Multiple Choice |
| Passing Score: | 65% |
| Available Languages: | English |
| Real Exam Qty: | 64 |
| Exam Duration: | 90 minutes |
| Exam Price: | USD 100 |
| Recommended Training: | IBM Training - SPSS Modeler Courses |
| Exam Registration: | IBM Certification Exams |
| Sample Questions: | IBM BAS-010 Sample Questions |
| Exam Way: | Online proctored exam or authorized testing center |
| Pre Condition: | Knowledge of data mining concepts, statistics fundamentals, and experience using IBM SPSS Modeler is recommended. |
| Official Syllabus URL: | https://www.ibm.com/training/certification |
IBM BAS-010 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: IBM SPSS Modeler Overview | - Understand SPSS Modeler architecture and components - Explain data mining concepts and CRISP-DM methodology |
| Topic 2: Modeling Techniques | - Unsupervised learning algorithms - Model evaluation and deployment - Supervised learning algorithms |
| Topic 3: Model Deployment and Administration | - Managing model results and scoring processes - Deploying predictive models |
| Topic 4: IBM SPSS Modeler Functionality | - Automation and scripting capabilities - Using modeling, output, and utility nodes - Streams and stream management |
| Topic 5: Data Preparation | - Using nodes for data preparation workflows - Data cleansing, transformation, and manipulation - Importing and accessing data sources |
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