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Last Updated: Aug 28, 2026
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The cost of the Data Quality 10 Developer, Professional Certification is $200. For more information related to exam price, please visit the official website Google Website as the cost of exams may be subjected to vary county-wise.
Preparation Guide for Data Quality 10 Developer, Professional Certification
Introduction for Data Quality 10 Developer, Professional Certification
This test measures your competency as a member of a project implementation team. This involves an in-depth knowledge of Data Quality processes such as Profiling, Standardization, Matching, and Consolidation. You must select and configure the appropriate Data Quality transformations and build, debug and execute Data Quality mappings including integrating those mappings into Power Center if needed.
The skills and knowledge areas measured by this test are focused on product core functionality inside the realm of a standard project implementation. Training materials, supporting documentation and practical experience may become sources of question development.
This is recommended for students seeking certification as a Data Quality Developer Certified Professional.
The suggested training prerequisites for this certification level are the completion of the following Informatica course(s):
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The following will be discussed in INFORMATICA DATA QUALITY 10 DEVELOPER SPECIALIST exam dumps:
The following will be discussed in INFORMATICA DATA QUALITY 10 DEVELOPER SPECIALIST exam dumps:
| Section | Weight | Objectives |
|---|---|---|
| Customization and Advanced Features | 8% | - DQ for Excel and other extensions - Custom transformations and rules |
| Matching and Consolidation | 25% | - Comparison and Match transformations - Grouping and match strategies - Duplicate resolution and golden record creation - Survivorship and consolidation rules |
| Data Quality Concepts and Architecture | 15% | - Informatica Data Quality (IDQ) architecture and components - Core data quality principles - Integration with PowerCenter and Analyst tool |
| Data Profiling | 20% | - Column and rule-based profiling - Analyzing profile results and quality metrics - Pattern discovery and data domain identification |
| Mapping Development and Execution | 12% | - Parameters, variables, and workflow integration - Building and testing IDQ mappings - Debugging and performance tuning |
| Standardization and Cleansing | 20% | - Reference tables and dictionary management - Parser, Standardizer, and Case Converter transformations - Address validation and cleansing |
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