Informatica Data-Quality-10-Developer-Specialist exam - 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 21, 2026
  • Q & A: 70 Questions and Answers
  • PDF Price: $59.99
  • PDF Demo

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  • Exam Code: Data-Quality-10-Developer-Specialist
  • Exam Name: Data Quality 10: Developer Specialist Exam
  • Updated: Aug 21, 2026
  • Q & A: 70 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $119.98  $79.99
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Informatica Data-Quality-10-Developer-Specialist exam - Testing Engine

Data-Quality-10-Developer-Specialist Testing Engine
  • Exam Code: Data-Quality-10-Developer-Specialist
  • Exam Name: Data Quality 10: Developer Specialist Exam
  • Updated: Aug 21, 2026
  • Q & A: 70 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

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Data Quality 10 Developer, Professional Certification Path

This is a fundamental exam and gives credential of Data Quality Developer Certified Professional.

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Topics of Data Quality 10 Developer, Professional Certification

INFORMATICA DATA QUALITY 10 DEVELOPER SPECIALIST exam dumps pdf will include the following topics:

  • Integration with PowerCenter
  • DQ for Excel
  • Standardization/Mapplets
  • Analyst Collaboration
  • Parameters
  • Consolidation and the DQA
  • Object Import and Export
  • Address Validation
  • Content
  • Informatica Overview
  • Profiling
  • Matching

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Understanding functional and technical aspects of Data Quality 10 Developer, Professional Certification

The following will be discussed in INFORMATICA DATA QUALITY 10 DEVELOPER SPECIALIST exam dumps:

  • Describe how to Export a Project
  • Differentiate between the various parsing techniques available
  • Explain Dependency conflict resolution and how it is handled
  • Describe the requirements for integration for Excel
  • Provide an explanation on how to use the Informatica ribbon in Excel
  • Define the techniques for creating mappings for DQ for Excel
  • Apply, configure and troubleshoot data standardization transformations
  • Define the difference between the Basic and Advanced Import options available
  • Recognize how reference tables are used in the standardization process
  • Explain the Web service capabilities required for DQ for Excel
  • Be aware of where data standardization fits in the DQ process
  • Perform and interpret column, rule, comparative and mid-stream profiling

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

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

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