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How to Prepare For IBM InfoSphere QualityStage Fundamentals Technical Mastery Test v1
Preparation Guide for IBM InfoSphere QualityStage Fundamentals Technical Mastery Test v1
Introduction
This exam validates the technical knowledge required for the purpose of identifying, managing and closing sales opportunities.The test is applicable to sales representatives who demonstrate sales and technical knowledge of the software and targets the technical sales professional who can deliver a comprehensive business solution to customers through solution identification, product differentiation, and competitive positioning.
Passing this test counts towards PartnerWorld competency requirements and helps you attain higher membership levels. This test also entitles you to use the mastery qualification title on your business card. IBM P2090-95 practice exams and IBM P2090-95 practice test have all questions related to this.
Candidate should give this exam who want:
- Understand general concept of data quality and QualityStage
- Understand the algorithms used in QualityStage to improve data quality
- Understand QualityStage commonly used components and processes
For more info read reference:
IBM P2090-95 Official Certification Site
How much IBM InfoSphere QualityStage Fundamentals Technical Mastery Test v1 costs
- Examination Name: IBM InfoSphere QualityStage Fundamentals Technical Mastery Test v1
- No of Questions: 41 Questions
- Length of Exam: 90 min
- Passing Score: 23 or higher
- Examination Fees: $180 USD
- Types of questions: Performance Based Questions
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IBM P2090-095 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Survivorship and Job Execution | 15% | - Outputs and reporting - Building and running QualityStage jobs - Survive stage and rules |
| Topic 2: Data Matching and Comparison | 25% | - Match stage concepts - Agreement and disagreement weights - Comparison methods and weighting |
| Topic 3: Introduction to InfoSphere QualityStage | 20% | - Architecture and components - Purpose and core capabilities - Data quality concepts and lifecycle |
| Topic 4: Data Profiling and Investigation | 20% | - Investigation stages and techniques - Tokenization and pattern analysis - Interpreting profiling results |
| Topic 5: Data Standardization | 20% | - Standardization concepts and workflow - Rule sets and classification - Parsing and formatting data |






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