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Read the Informatica Data Quality 9.x Developer (PR000005) Certification Exam Requirements below
There is no perquisites for Informatica Data Quality 9.x Developer (PR000005) exam.
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Informatica Data Quality 9.x Developer (PR000005) Exam
This PR000005 exam is related to the Informatica Data Quality 9.x Developer Certification. This exam is designed for Developers who are new to Informatica Data Quality. Informatica Developer is a frequently used desktop client tool that developers can use to profile, clean, standardize, duplicate and consolidate data in a business. This exam also deals with the ability to working with the Developer Tool, performing tasks such as creating projects and objects, profiling data and identifying anomalies.
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Informatica provides the market-leading data integration platform. There are 500,000 combinations of applications and platforms tested. The data integration platform works with the widest possible range of disparate standards, systems, and applications. This impartial and universal vision makes Informatica unique on the current market as a leader in the data integration platform. So, Informatica becomes the ideal strategic platform for companies wishing to solve data integration problems of any size.
To Pass Data Quality 9.x Developer Specialist Certification, a candidate must pass a written, supervised in-person or webcam exam. The specialized test confirms that the person understands the product and can apply the skills and abilities necessary to contribute to a project as a member of the complete team.
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Informatica PR000005 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Address Validation & Enrichment | 15% | - Configure address validation transformation - Validate and standardize global addresses - Enrich data with reference data |
| Topic 2: Project & Workflow Development | 15% | - Work with parameters and mappings - Use Informatica Developer tool - Collaborate and deploy projects - Manage dependencies and resolution |
| Topic 3: Data Matching & Consolidation | 25% | - Tune match strategies and thresholds - Design and build match plans - Configure Identity Matching options - Create survivor records and consolidate duplicates |
| Topic 4: Data Profiling | 20% | - Analyze profiling results and identify anomalies - Understand data quality metrics and patterns - Configure and run data profiling |
| Topic 5: Data Standardization & Cleansing | 25% | - Troubleshoot standardization logic - Configure standardization transformations - Implement parsing and normalization rules |
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