Informatica PR000005 : Data Quality 9.x Developer Specialist

  • Exam Code: PR000005
  • Exam Name: Data Quality 9.x Developer Specialist
  • Updated: Oct 13, 2026
  • Q & A: 70 Questions and Answers

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Read the registration procedure of the Informatica Data Quality 9.x Developer (PR000005) Certification Exam

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  • Login/register at Webassessor for test taker account
  • We recommend that you register for 3 months in advance to get your preferred times and enough time to prepare.

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Reference: https://www.informatica.com/services-and-training/certification/data-quality-certification.html#fbid=M68tJ9Yt3El

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Informatica provides the market-leading data integration platform. There are 500,000 combinations of applications and platforms tested. The data integration platform works with the widest possible range of disparate standards, systems, and applications. This impartial and universal vision makes Informatica unique on the current market as a leader in the data integration platform. So, Informatica becomes the ideal strategic platform for companies wishing to solve data integration problems of any size.

To Pass Data Quality 9.x Developer Specialist Certification, a candidate must pass a written, supervised in-person or webcam exam. The specialized test confirms that the person understands the product and can apply the skills and abilities necessary to contribute to a project as a member of the complete team.

Informatica PR000005 Exam Syllabus Topics:

SectionObjectives
Topic 1: Data Cleansing and Validation- Improve data quality
  • 1. Create validation rules
    • 2. Handle exceptions
      • 3. Cleanse and enrich data
        Topic 2: Developer Tool and Mappings- Build Data Quality solutions
        • 1. Deploy and execute applications
          • 2. Create mappings and mapplets
            • 3. Configure transformations
              Topic 3: Matching and Consolidation- Implement matching strategies
              • 1. Use probabilistic and deterministic matching
                • 2. Group and consolidate duplicate records
                  • 3. Configure match rules
                    Topic 4: Data Quality Applications- Implement enterprise data quality processes
                    • 1. Web service deployment
                      • 2. Data quality monitoring and reporting
                        • 3. Data Quality for Excel integration
                          Topic 5: Data Profiling- Analyze data quality
                          • 1. Profile primary and foreign keys
                            • 2. Profile column data
                              • 3. Identify data anomalies and patterns
                                Topic 6: Data Standardization- Standardize and parse data
                                • 1. Use Standardizer transformation
                                  • 2. Use Token Parser and Pattern Parser
                                    • 3. Apply reference tables and dictionaries

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