Read the Informatica Data Quality 9.x Developer (PR000005) Certification Exam Requirements below
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Reference: https://www.informatica.com/services-and-training/certification/data-quality-certification.html#fbid=M68tJ9Yt3El
A brief introduction to the course
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Concise contents
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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.
A true simulation environment
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Informatica PR000005 Exam Syllabus Topics:
| Section | Objectives |
| Topic 1: Data Standardization | - Standardize and parse data
- 1. Use Standardizer transformation
- 2. Use Token Parser and Pattern Parser
- 3. Apply reference tables and dictionaries
|
| Topic 2: Data Profiling | - Analyze data quality
- 1. Profile column data
- 2. Identify data anomalies and patterns
- 3. Profile primary and foreign keys
|
| Topic 3: Developer Tool and Mappings | - Build Data Quality solutions
- 1. Deploy and execute applications
- 2. Configure transformations
- 3. Create mappings and mapplets
|
| Topic 4: Matching and Consolidation | - Implement matching strategies
- 1. Configure match rules
- 2. Group and consolidate duplicate records
- 3. Use probabilistic and deterministic matching
|
| Topic 5: Data Quality Applications | - Implement enterprise data quality processes
- 1. Data Quality for Excel integration
- 2. Data quality monitoring and reporting
- 3. Web service deployment
|
| Topic 6: Data Cleansing and Validation | - Improve data quality
- 1. Create validation rules
- 2. Handle exceptions
- 3. Cleanse and enrich data
|