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How much Data Quality 10 Developer, Professional Certification Cost

The cost of the Data Quality 10 Developer, Professional Certification is $200. For more information related to exam price, please visit the official website Google Website as the cost of exams may be subjected to vary county-wise.

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: Data Quality Management for Developers
  • Informatica Analyst
  • Data Quality: Advanced Techniques

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.

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Who should take the Data Quality 10 Developer, Professional Certification

People who are familiar with:

  • Executing mappings in PowerCenter
  • Deploying Data Quality Mappings in an Excel spreadsheet (connect to Web Services)
  • Automate and manual consolidation of duplicate records into a master record
  • Converting a profile into a mapping
  • Project Collaboration techniques such as Tags and Comments
  • Identifying duplicate records
  • Performing Address Validation
  • Using mappings and mapplets to cleanse and standardize data
  • Navigation through the Developer Tool
  • Profiling methods to identify data irregularities and inaccuracies
  • Using Profiles and reference tables to share information with Analysts

Peoplw who want to learn how to run data quality mapping on Excel spread sheets in this module (Power Center Integration, Performing Consolidation, grouping data and DQ Matching Cluster Analysis, GUI, Mappings Mapplets and Reusable AV Transformation.etc) and identify, resolve, and prevent costly data problems can consult INFORMATICA DATA QUALITY 10 DEVELOPER SPECIALIST practice exam and INFORMATICA DATA QUALITY 10 DEVELOPER SPECIALIST practice exams.

With a number of opportunities in the field, the following job roles will get benefited from this exam:

  • Business Analysts
  • Data Explorers
  • Candidates interested in taking up career under Informatica
  • Analytics Professionals
  • Mainframe Developers and Architects
  • Business Developers

Informatica Data-Quality-10-Developer-Specialist Exam Syllabus Topics:

SectionWeightObjectives
Integration with PowerCenter5%- Execution and Deployment
  • 1. Running mappings in PowerCenter
    • 2. Integration workflows
      DQ for Excel5%- Excel-based Data Quality
      • 1. Web service integration with Excel
        Analyst Collaboration10%- Analyst Tool Usage
        • 1. Scorecards, profiling, filters, tags
          • 2. Collaboration between Analyst and Developer
            Standardization and Mapplets10%- Data Standardization
            • 1. Reference tables in standardization
              • 2. Standardizer transformation usage
                • 3. Parsing techniques
                  Object Import and Export5%- Metadata Management
                  • 1. Import/export of mappings and objects
                    Informatica Platform Overview10%- Architecture and Components
                    • 1. Informatica Data Quality architecture concepts
                      • 2. Repository and services setup
                        Content10%- Prebuilt Content Usage
                        • 1. Accelerators and reference data
                          Address Validation5%- Address Validation Transformation
                          • 1. Validation modes and configuration
                            • 2. Output interpretation
                              Matching10%- Match Logic
                              • 1. Match rules and strategies
                                • 2. Identity matching concepts
                                  Consolidation and Data Quality Applications (DQA)10%- Data Consolidation
                                  • 1. Duplicate record handling
                                    • 2. Master record creation
                                      Parameters5%- Runtime Configuration
                                      • 1. Parameter files and runtime variables
                                        Data Profiling15%- Profiling Methods
                                        • 1. Comparative profiling
                                          • 2. Column profiling
                                            • 3. Rule-based profiling

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