How to Prepare For Data Quality 10 Developer, Professional Certification
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):
- Informatica Analyst
- Data Quality: Advanced Techniques
- Data Quality: Data Quality Management for Developers
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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:
- Standardization/Mapplets
- Matching
- Informatica Overview
- Parameters
- Content
- Profiling
- DQ for Excel
- Address Validation
- Object Import and Export
- Consolidation and the DQA
- Analyst Collaboration
- Integration with PowerCenter
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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.
Informatica Data-Quality-10-Developer-Specialist Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Mapping Development and Execution | 12% | - Parameters, variables, and workflow integration - Building and testing IDQ mappings - Debugging and performance tuning |
| Matching and Consolidation | 25% | - Comparison and Match transformations - Duplicate resolution and golden record creation - Survivorship and consolidation rules - Grouping and match strategies |
| Standardization and Cleansing | 20% | - Parser, Standardizer, and Case Converter transformations - Address validation and cleansing - Reference tables and dictionary management |
| Customization and Advanced Features | 8% | - DQ for Excel and other extensions - Custom transformations and rules |
| Data Profiling | 20% | - Analyzing profile results and quality metrics - Pattern discovery and data domain identification - Column and rule-based profiling |
| Data Quality Concepts and Architecture | 15% | - Core data quality principles - Informatica Data Quality (IDQ) architecture and components - Integration with PowerCenter and Analyst tool |
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