IBM BAS-013日本語 Exam Details & Actual Exam Questions
- Exam Code/Number: BAS-013日本語
- Exam Name/Title: IBM SPSS Modeler Data Mining for Business Partners v2 (BAS-013日本語版)
- Certification Provider: IBM
- Corresponding Certification: IBM Certified Associate
- Exam Questions: 25
- Updated On: Sep,03 2026
- Certification Level: Associate
IBM SPSS Modeler Data Mining for Business Partners v2 (BAS-013日本語版) Exam Questions
View BAS-013日本語 actual exam questions, answers and explanations for free.
92% student found the test questions almost same
All the information you need to pass IBM SPSS Modeler Data Mining for Business Partners v2 (BAS-013日本語版) BAS-013日本語 exam and free practice exam verified by EduDump exam experts.
Said the test questions were almost same
Passed the exams with the material
Found the study quides effective and helpful
This exam has been stopped to register, new exam code replace: C2090-013J
IBM BAS-013日本語 Exam Overview:
| Certification Vendor: | IBM |
|---|---|
| Exam Name: | IBM SPSS Modeler Data Mining for Business Partners v2 |
| Exam Number: | BAS-013 |
| Passing Score: | 66% |
| Certificate Validity Period: | 3 years |
| Exam Format: | Multiple Choice |
| Real Exam Qty: | 25 |
| Exam Price: | $200 USD |
| Available Languages: | English, Japanese |
| Exam Duration: | 60 minutes |
| Recommended Training: | IBM SPSS Modeler Official Training |
| Exam Registration: | IBM Certification Registration Pearson VUE |
| Sample Questions: | IBM BAS-013日本語 Sample Questions |
| Exam Way: | Online proctored or authorized test center |
| Pre Condition: | No formal prerequisites; basic knowledge of SPSS Modeler and data mining concepts recommended |
| Official Syllabus URL: | https://www-03.ibm.com/certify/exam.html?id=C2090-013 |
IBM BAS-013日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Modeling & Evaluation | 20% | |
| Business Understanding | 15% | |
| Data Preparation | 35% | - Restructuring data - Creating new variables with Reclassify Node - General Operations in Modeler - Automated data preparation - Aggregating data - Combining data files - Creating new variables with Derive Node |
| Data Understanding | 20% | |
| Deployment & Interpretation | 10% |
0
0
0
10