SAS Certified Data Quality Steward for SAS 9 Practice Exam

The SAS Certified Data Quality Steward for SAS 9 certification is designed for professionals who are responsible for ensuring the quality of data within their organization. This certification validates the candidate's ability to implement and manage data quality practices using SAS tools. The SAS certification includes data profiling, cleansing, and monitoring, to assess data integrity and data quality. The certification attests to your expertise in data quality management, as needed in data governance and analytics teams.
Why is SAS Certified Data Quality Steward for SAS 9 important?

• Validates expertise in data quality management using SAS tools.
• Enhances career opportunities in data governance and analytics.
• Demonstrates ability to implement effective data quality practices.
• Supports organizations in achieving high standards of data integrity.
• Provides recognition of skills in data profiling, cleansing, and monitoring.

Who should take the SAS Certified Data Quality Steward for SAS 9 Exam?

• Data Quality Analyst
• Data Steward
• Data Governance Specialist
• Business Intelligence Analyst
• Data Analyst
• Data Management Consultant

Skills Evaluated

Candidates taking the certification exam on the SAS Certified Data Quality Steward for SAS 9 is evaluated for the following skills:

• Proficiency in data profiling techniques.
• Ability to cleanse and enrich data for quality improvement.
• Skills in monitoring data quality metrics and dashboards.
• Knowledge of data governance principles and practices.
• Competence in using SAS tools for data quality assessments.

SAS Certified Data Quality Steward for SAS 9 Certification Course Outline
The SAS Certified Data Quality Steward for SAS 9 Certification covers the following topics -

Module 1. Navigating the DataFlux Data Management Studio Interface

• Navigate within the Data Management Studio Interface

Module 2. Exploring and Profiling data

• Create and explore a data profile

• Design data standardization schemes

Module 3. Data Jobs

• Form Data Jobs

• Apply a Standardization definition and scheme

• Apply Parsing definitions

• Compare and contrast the differences between identification analysis and right fielding nodes

• Apply Casing definitions

• Apply the Gender Analysis node to determine gender

• Create an Entity Resolution Job

• Use data job references within a data job

• Understand how to use an Extraction definition

• Explain the process of the definition of pattern analysis

Module 4. Business Rules Monitoring

• Explain and create business rules

• Create new tasks

Module 5. Data Management Server

• Interact with the Data Management Server

Module 6. Expression Engine Language (EEL)

• Explain the basic structure of EEL (components and syntax)

Module 7. Process Jobs

• Work with and create process jobs

Module 8. Macro Variables and Advanced Properties and Settings

• Work with and use macro variables in data profiles, data jobs and data monitoring

• Determine uses for advanced properties

Module 9. Quality Knowledge Base (QKB)

• Explain the organization, structure and basic navigation of the QKB

• Be able to articulate when to use the various components of the QKB

• Explain the processing steps and components used in the different definition types.