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Who should take the Data Quality 10 Developer, Professional Certification
People who are familiar with:
- Project Collaboration techniques such as Tags and Comments
- Automate and manual consolidation of duplicate records into a master record
- Identifying duplicate records
- Navigation through the Developer Tool
- Using Profiles and reference tables to share information with Analysts
- Performing Address Validation
- Executing mappings in PowerCenter
- Using mappings and mapplets to cleanse and standardize data
- Converting a profile into a mapping
- Profiling methods to identify data irregularities and inaccuracies
- Deploying Data Quality Mappings in an Excel spreadsheet (connect to Web Services)
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 Developers
- Business Analysts
- Analytics Professionals
- Mainframe Developers and Architects
- Candidates interested in taking up career under Informatica
- Data Explorers
Understanding functional and technical aspects of Data Quality 10 Developer, Professional Certification
The following will be discussed in INFORMATICA DATA QUALITY 10 DEVELOPER SPECIALIST exam dumps:
- Verify the importance of Address Validation
- Develop and build match plans to identify duplicate or related data
- Identify which Transformations can use parameters
- Configure Identity Matching option for the match transformation
- Describe the process of exporting mappings with parameters built in
- Configure the Address Validation transformation including explaining each mode that is available and what custom templates can be used for
- Explain what parameters are and why they are used including what parameter functions are supported
- Differentiate between the matching algorithms available and explain how matching scores are
- Interpret the AV outputs generated
- Explain populations, the identity match strategies that are available and how they are used in Identity Matching
- 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
Understanding functional and technical aspects of Data Quality 10 Developer, Professional Certification
The following will be discussed in INFORMATICA DATA QUALITY 10 DEVELOPER SPECIALIST exam dumps:
- Explain how to integrate IDQ mappings and mapplets into PowerCenter Workflows including how content is handled
- Remove duplicates using DQA
- Describe the Informatica 10 architecture and set up including the repositories required for installation
- Provide information on the Data Quality process and dimensions of data quality
- Explain Automatic and Manual consolidation techniques and configure the Transformations used for automatic consolidation
- Describe the benefits of project collaboration to a team
- Use and describe the functionality of the Analyst Tool including Scorecarding, Reference Table Management, Tags, Filters, Profiles and Comments
- Generate a survivor record in the DQA
- Troubleshoot any problems that may occur
- Use the Exception Transformation to generate and populate the Bad Records and Duplicate Records tables
- Describe how Analysts and Developers can collaborate on projects
Informatica Data-Quality-10-Developer-Specialist Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Integration with PowerCenter | 5% | - Execution and Deployment
|
| Standardization and Mapplets | 10% | - Data Standardization
|
| Consolidation and Data Quality Applications (DQA) | 10% | - Data Consolidation
|
| Content | 10% | - Prebuilt Content Usage
|
| Data Profiling | 15% | - Profiling Methods
|
| Object Import and Export | 5% | - Metadata Management
|
| Analyst Collaboration | 10% | - Analyst Tool Usage
|
| Parameters | 5% | - Runtime Configuration
|
| Informatica Platform Overview | 10% | - Architecture and Components
|
| DQ for Excel | 5% | - Excel-based Data Quality
|
| Matching | 10% | - Match Logic
|
| Address Validation | 5% | - Address Validation Transformation
|
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