Learn with IBM : M2020-229 training material for 100% pass
Last Updated: Sep 13, 2026
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IBM M2020-229 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: IBM SPSS Predictive Analytics Overview | - Core capabilities of SPSS predictive solutions - Introduction to IBM SPSS Predictive Analytics portfolio - Business value of predictive analytics |
| Topic 2: Solution Positioning and Sales Strategy | - Positioning IBM SPSS against competing solutions - Value-based selling approach - Identifying customer use cases for predictive analytics |
| Topic 3: Sales Methodology for IBM SPSS | - Mapping solutions to business outcomes - Objection handling and value justification - Understanding customer pain points |
| Topic 4: Industry Applications | - Customer analytics and segmentation - Risk management and fraud detection - Marketing optimization and campaign analytics |
| Topic 5: IBM SPSS Use Cases | - Operational decision-making support - Predictive modeling and forecasting scenarios |
IBM SPSS Predictive Analytics Sales Mastery v1 Sample Questions:
A customer wants to develop a compelling survey, mine the unstructured responses for meaningful trends and relationships, and then deploy the results. Which SPSS product is NOT required to drive this goal?
- A. SPSS Modeler Premium
- B. SPSS Collaboration and Deployment Services
- C. SPSS Statistics
- D. SPSS Data Collection
Correct Answer: C 🗳️
Which characteristic of SPSS Collaboration and Deployment Services appeals most to expert analysts?
- A. Enterprise readiness
- B. Integration with existing environment
- C. Ability to automate processes
- D. Collaboration with business users
Correct Answer: D 🗳️
What is the major advantage of SPSS Modeler over SAS Base and Enterprise Miner?
- A. Structured data mining capabilities.
- B. Support for multiple operating systems.
- C. Easy to use interface for business-oriented analysts.
- D. More powerful in the hands of a modeling expert.
Correct Answer: C 🗳️
What is NOT a key competitive differentiator of SPSS Modeler based on product feature/functionality?
- A. Scorecards and dashboards.
- B. Intuitive visual interface.
- C. Automated modeling and data preparation.
- D. Open and scalable architecture.
Correct Answer: A 🗳️
A customer wants to optimize their data for simpler, more accurate, and faster models. Which family of algorithms/techniques best meets their needs?
- A. Classification
- B. Reduction
- C. Segmentation
- D. Association
Correct Answer: A 🗳️
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