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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:
- Profiling
- Content
- Parameters
- Standardization/Mapplets
- Informatica Overview
- Consolidation and the DQA
- Integration with PowerCenter
- Object Import and Export
- Analyst Collaboration
- DQ for Excel
- Address Validation
- Matching
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The following will be discussed in INFORMATICA DATA QUALITY 10 DEVELOPER SPECIALIST exam dumps:
- Configure Identity Matching option for the match transformation
- Explain populations, the identity match strategies that are available and how they are used in Identity Matching
- Interpret the AV outputs generated
- Configure the Address Validation transformation including explaining each mode that is available and what custom templates can be used for
- Describe the process of exporting mappings with parameters built in
- Differentiate between the matching algorithms available and explain how matching scores are
- Identify which Transformations can use parameters
- Explain what Content is, what is contained in the Core Accelerator and why it is used in the Core Accelerator and why it is used
- Explain what parameters are and why they are used including what parameter functions are supported
- Develop and build match plans to identify duplicate or related data
- Verify the importance of Address Validation
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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 |
|---|---|---|
| Topic 1: Data Quality Concepts and Architecture | 15% | - Integration with PowerCenter and Analyst tool - Core data quality principles - Informatica Data Quality (IDQ) architecture and components |
| Topic 2: Customization and Advanced Features | 8% | - DQ for Excel and other extensions - Custom transformations and rules |
| Topic 3: Data Profiling | 20% | - Column and rule-based profiling - Analyzing profile results and quality metrics - Pattern discovery and data domain identification |
| Topic 4: Matching and Consolidation | 25% | - Comparison and Match transformations - Duplicate resolution and golden record creation - Grouping and match strategies - Survivorship and consolidation rules |
| Topic 5: Mapping Development and Execution | 12% | - Debugging and performance tuning - Building and testing IDQ mappings - Parameters, variables, and workflow integration |
| Topic 6: Standardization and Cleansing | 20% | - Reference tables and dictionary management - Parser, Standardizer, and Case Converter transformations - Address validation and cleansing |
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