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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
- Duration of Exam: 90 minutes
- No negative marking for wrong answers
- Language of Exam: English, Japanese, Korean
- Passing grade: 70%
- Type of Questions: Multiple choice (MCQs), multiple answers
- Number of questions: 70
How to study the Data Quality 10 Developer, Professional Certification
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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):
- Data Quality: Advanced Techniques
- Informatica Analyst
- Data Quality: Data Quality Management for Developers
We prepare INFORMATICA DATA QUALITY 10 DEVELOPER SPECIALIST practice exams and INFORMATICA DATA QUALITY 10 DEVELOPER SPECIALIST practice exams to prepare you for all these requirements.
Informatica Data-Quality-10-Developer-Specialist Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Concepts and Architecture | 15% | - Core data quality principles - Integration with PowerCenter and Analyst tool - Informatica Data Quality (IDQ) architecture and components |
| Topic 2: Matching and Consolidation | 25% | - Survivorship and consolidation rules - Grouping and match strategies - Duplicate resolution and golden record creation - Comparison and Match transformations |
| Topic 3: Mapping Development and Execution | 12% | - Parameters, variables, and workflow integration - Debugging and performance tuning - Building and testing IDQ mappings |
| Topic 4: Data Profiling | 20% | - Analyzing profile results and quality metrics - Pattern discovery and data domain identification - Column and rule-based profiling |
| Topic 5: Standardization and Cleansing | 20% | - Reference tables and dictionary management - Parser, Standardizer, and Case Converter transformations - Address validation and cleansing |
| Topic 6: Customization and Advanced Features | 8% | - DQ for Excel and other extensions - Custom transformations and rules |
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