IBM SPSS Predictive Analytics Sales Mastery v1: M2020-229 Exam


"IBM SPSS Predictive Analytics Sales Mastery v1", also known as M2020-229 exam, is a IBM Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 45 Q&As to your M2020-229 Exam preparation. In the M2020-229 exam resources, you will cover every field and category in Sales Mastery Certification helping to ready you for your successful IBM Certification.

  • Exam Code: M2020-229
  • Exam Name: IBM SPSS Predictive Analytics Sales Mastery v1
  • Total Questions: 45
  • Certification Provider: IBM
  • Corresponding Certification: Sales Mastery
  • Updated on: Jul 13, 2026

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IBM SPSS Predictive Analytics Sales Mastery v1 Sample Questions:

1. Which statement is true about automated modeling?

A) It is more about data preparation than modeling.
B) It automates the whole data mining process from data access through deployment.
C) It is a specific type of algorithm that makes data simpler.
D) It creates many models for the user to evaluate the best model in a single step.


2. What is NOT a key benefit of SPSS Statistics based on product feature/functionality?

A) Accessibility of mathematical principles.
B) Flexibility of multiple interfaces to key functions.
C) Multiple deployment capabilities.
D) A comprehensive set of data mining tools.


3. Which statement is true about SPSS Text Analytics?

A) It allows users to import custom rules and categories.
B) It requires new users to build categories and dictionaries from scratch before it can be used.
C) It confirms model accuracy.
D) It supports over 100 languages.


4. A customer wants to optimize their data for simpler, more accurate, and faster models. Which family of algorithms/techniques best meets their needs?

A) Segmentation
B) Association
C) Classification
D) Reduction


5. Which SPSS product family is more focused on the Act phase of the predictive analytics process?

A) Statistics
B) Data Collection
C) Deployment
D) Modeling


Solutions:

Question # 1
Answer: C
Question # 2
Answer: D
Question # 3
Answer: B
Question # 4
Answer: C
Question # 5
Answer: D

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