SPSS IBM SPSS Modeler - Business Partner Data Analyst Associate IBMSPSSMBPDA Certified Exam Dumps

IBMSPSSMBPDA Exam Dumps

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Certification Provider: SPSS
Exam Code / Number: IBMSPSSMBPDA
Exam Name: IBM SPSS Modeler - Business Partner Data Analyst Associate Exam
Exam Questions: 25
Last Updated: Jun 21, 2026
Corresponding Certification: SPSS Certification

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SPSS IBMSPSSMBPDA Exam Syllabus Topics:

SectionObjectives
Topic 1: Data Preparation and Transformation- Data cleansing techniques
  • 1. Outlier detection and treatment
    • 2. Handling missing values
      - Data transformation methods
      • 1. Deriving new fields
        • 2. Normalization and aggregation
          Topic 2: Model Evaluation and Validation- Model validation techniques
          • 1. Cross-validation concepts
            • 2. Training and testing split
              - Model performance metrics
              • 1. ROC and lift charts
                • 2. Accuracy and confusion matrix
                  Topic 3: IBM SPSS Modeler Fundamentals- Data understanding and profiling
                  • 1. Data quality assessment
                    • 2. Data types and structures
                      - Introduction to SPSS Modeler interface
                      • 1. Streams and nodes overview
                        • 2. Data preparation basics
                          Topic 4: Business Application of Analytics- Business problem framing
                          • 1. Use case identification
                            • 2. Translating business needs into models
                              - Deployment considerations
                              • 1. Reporting and insights delivery
                                • 2. Operationalizing models
                                  Topic 5: Predictive Modeling- Clustering and segmentation
                                  • 1. K-means clustering
                                    • 2. Customer segmentation use cases
                                      - Classification techniques
                                      • 1. Decision trees
                                        • 2. Logistic regression basics


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