Informatica PR000005 Exam Details & Actual Exam Questions

  • Exam Code/Number: PR000005
  • Exam Name/Title: Data Quality 9.x Developer Specialist
  • Certification Provider: Informatica
  • Corresponding Certification: Developer Specialist
  • Exam Questions: 70
  • Updated On: Jun,14 2026
  • Certification Level: Specialist

Informatica Data Quality 9.x Developer Specialist Exam Questions

View PR000005 actual exam questions, answers and explanations for free.

users 94% student found the test questions almost same

All the information you need to pass Informatica Data Quality 9.x Developer Specialist PR000005 exam and free practice exam verified by EduDump exam experts.

Said the test questions were almost same
Passed the exams with the material
Found the study quides effective and helpful
(14 Up Votes)

Informatica PR000005 Exam Overview:

Certification Vendor:Informatica
Exam Name:Data Quality 9.x Developer Specialist
Exam Number:PR000005
Related Certifications:Informatica Certified Professional (ICP) - Data Quality 9.x Developer Specialist
Available Languages:English
Certificate Validity Period:This certification exam has been retired.
Exam Format:Multiple Choice
Sample Questions:Informatica PR000005 Sample Questions
Exam Way:Delivered through Informatica's authorized certification platform (Webassessor) as an online proctored or authorized testing exam, depending on availability at the time. The exam has since been retired.
Pre Condition:No mandatory prerequisite exam was officially required. Informatica recommended candidates possess practical experience with Informatica Data Quality 9.x and related developer training.
Official Syllabus URL:https://www.informatica.com/services-and-training/certification.html

Informatica PR000005 Exam Syllabus Topics:

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


                                      0
                                      0
                                      0
                                      10