Get real Data-Quality-10-Developer-Specialist exam questions for better preparation

Real Informatica Data-Quality-10-Developer-Specialist practice exam questions for easy pass!

Last Updated: Aug 25, 2026

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Informatica Data-Quality-10-Developer-Specialist Practice Q&A's

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How to book the Data Quality 10 Developer, Professional Certification

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

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

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

SectionWeightObjectives
Customization and Advanced Features8%- DQ for Excel and other extensions
- Custom transformations and rules
Data Quality Concepts and Architecture15%- Informatica Data Quality (IDQ) architecture and components
- Integration with PowerCenter and Analyst tool
- Core data quality principles
Standardization and Cleansing20%- Address validation and cleansing
- Reference tables and dictionary management
- Parser, Standardizer, and Case Converter transformations
Mapping Development and Execution12%- Building and testing IDQ mappings
- Debugging and performance tuning
- Parameters, variables, and workflow integration
Data Profiling20%- Pattern discovery and data domain identification
- Column and rule-based profiling
- Analyzing profile results and quality metrics
Matching and Consolidation25%- Survivorship and consolidation rules
- Comparison and Match transformations
- Duplicate resolution and golden record creation
- Grouping and match strategies

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