Data-Quality-10-Developer-Specialist Q&As - in .pdf

Data-Quality-10-Developer-Specialist pdf
  • Exam Code: Data-Quality-10-Developer-Specialist
  • Exam Name: Data Quality 10: Developer Specialist Exam
  • Updated: Aug 22, 2026
  • Q & A: 70 Questions and Answers
  • PDF Price: $59.99
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  • Exam Code: Data-Quality-10-Developer-Specialist
  • Exam Name: Data Quality 10: Developer Specialist Exam
  • Updated: Aug 22, 2026
  • Q & A: 70 Questions and Answers
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  • Exam Code: Data-Quality-10-Developer-Specialist
  • Exam Name: Data Quality 10: Developer Specialist Exam
  • Updated: Aug 22, 2026
  • Q & A: 70 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

About Informatica Data-Quality-10-Developer-Specialist Exam Study Material

To be eligible for the Informatica Data-Quality-10-Developer-Specialist exam, candidates must have a minimum of two years of experience in developing data quality solutions using the Informatica Data Quality 10 platform. They must also have a good understanding of data quality concepts and best practices. Candidates who pass the exam will receive the Informatica Data Quality 10: Developer Specialist certification, which is recognized by many organizations as a mark of excellence in the field of data quality development.

The Informatica Data-Quality-10-Developer-Specialist exam is divided into two main sections: Data Quality Developer Fundamentals and Advanced Developer Techniques. The Fundamentals section covers topics such as data profiling, data standardization, data cleansing, and data parsing. The Advanced Developer Techniques section covers topics such as data matching, survivorship, consolidation, and scorecards. Data-Quality-10-Developer-Specialist exam is designed to test the ability of developers to apply their knowledge of IDQ 10 to solve real-world data quality problems. Those who pass the exam will be recognized as Informatica Certified Data Quality Developer Specialists, which will enhance their career prospects and increase their credibility in the industry.

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Informatica Data-Quality-10-Developer-Specialist Exam Syllabus Topics:

SectionWeightObjectives
Integration with PowerCenter5%- Execution and Deployment
  • 1. Integration workflows
    • 2. Running mappings in PowerCenter
      Address Validation5%- Address Validation Transformation
      • 1. Output interpretation
        • 2. Validation modes and configuration
          Standardization and Mapplets10%- Data Standardization
          • 1. Standardizer transformation usage
            • 2. Parsing techniques
              • 3. Reference tables in standardization
                Matching10%- Match Logic
                • 1. Match rules and strategies
                  • 2. Identity matching concepts
                    DQ for Excel5%- Excel-based Data Quality
                    • 1. Web service integration with Excel
                      Object Import and Export5%- Metadata Management
                      • 1. Import/export of mappings and objects
                        Analyst Collaboration10%- Analyst Tool Usage
                        • 1. Scorecards, profiling, filters, tags
                          • 2. Collaboration between Analyst and Developer
                            Parameters5%- Runtime Configuration
                            • 1. Parameter files and runtime variables
                              Content10%- Prebuilt Content Usage
                              • 1. Accelerators and reference data
                                Informatica Platform Overview10%- Architecture and Components
                                • 1. Informatica Data Quality architecture concepts
                                  • 2. Repository and services setup
                                    Data Profiling15%- Profiling Methods
                                    • 1. Rule-based profiling
                                      • 2. Comparative profiling
                                        • 3. Column profiling
                                          Consolidation and Data Quality Applications (DQA)10%- Data Consolidation
                                          • 1. Duplicate record handling
                                            • 2. Master record creation

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