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

  • Exam Code: Data-Quality-10-Developer-Specialist
  • Exam Name: Data Quality 10: Developer Specialist Exam
  • Updated: Aug 15, 2026
  • Q & A: 70 Questions and Answers

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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.

What is the duration, language, and format of Data Quality 10 Developer, Professional Certification

  • Language of Exam: English, Japanese, Korean
  • Number of questions: 70
  • Type of Questions: Multiple choice (MCQs), multiple answers
  • Passing grade: 70%
  • Duration of Exam: 90 minutes
  • No negative marking for wrong answers

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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:

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

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

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

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