IBM P2090-095 Exam Details & Actual Exam Questions

  • Exam Code/Number: P2090-095
  • Exam Name/Title: IBM InfoSphere QualityStage Fundamentals Technical Mastery Test v1
  • Certification Provider: IBM
  • Corresponding Certification: IBM Mastery
  • Exam Questions: 41
  • Updated On: Jul,16 2026
  • Certification Level: Associate

IBM InfoSphere QualityStage Fundamentals Technical Mastery Test v1 Exam Questions

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IBM P2090-095 Exam Overview:

Certification Vendor:IBM
Exam Name:IBM InfoSphere QualityStage Fundamentals Technical Mastery Test v1
Exam Number:P2090-095
Related Certifications:IBM DataStage certification
IBM InfoSphere Information Server certification
Real Exam Qty:30-50
Passing Score:60-70%
Exam Duration:60-90
Exam Format:Multiple choice, Scenario-based questions
Exam Price:USD 150-200 (varies by region)
Available Languages:English
Certificate Validity Period:3 years (typical IBM digital badge validity)
Sample Questions:IBM P2090-095 Sample Questions
Exam Way:Online proctored exam (typically via Pearson VUE or IBM authorized testing partner)
Pre Condition:No strict prerequisites. Basic knowledge of data integration and IBM InfoSphere DataStage is recommended.
Official Syllabus URL:https://www.ibm.com/training/certification

IBM P2090-095 Exam Syllabus Topics:

SectionObjectives
Topic 1: InfoSphere QualityStage Architecture- Components and workflow
  • 1. Parallel job design concepts
    • 2. QualityStage stages overview
      Topic 2: Matching and Linking- Match design principles
      • 1. Deterministic vs probabilistic matching
        • 2. Match specifications and scoring
          Topic 3: Standardization and Parsing- Data standardization techniques
          • 1. Rule sets and reference data usage
            • 2. Parsing unstructured data
              Topic 4: Data Cleansing and Survivorship- Record consolidation concepts
              • 1. Duplicate resolution strategies
                • 2. Survivorship rules
                  Topic 5: Data Quality Fundamentals- Core concepts of data quality
                  • 1. Accuracy, completeness, consistency concepts
                    • 2. Data profiling fundamentals


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