CDMP Data Quality Practice Questions Practitioner/Master

Are you looking to take your data management skills to the next level and stand out in a rapidly growing field? Look no further than the Certified Data Management Professional (CDMP) certification.

The CDMP Exam has following levels and requriements:

• Associate: 60% pass Data Management Fundamentals exam
• Practitioner: 70% pass in Data Management Fundamentals exam and 70% pass in 2 specialist exams
• Master: 80% pass in Data Management Fundamentals exam and 80% pass in 2 specialist exams

This question set prepares you for one of the specialist exams: Data Quality Management

As one of the youngest professions, data management is in need of mature standards and expected performance to improve outcomes over time. The CDMP certification provides a globally recognized standard for data management professionals, similar to PMI and ITIL certifications in project and service management.

This CDMP certification will provide you with a comprehensive understanding of data management principles and practices, including data architecture, governance, integration, quality, and security. You will gain the technical and analytical skills necessary to excel in a career in data management. Additionally, the CDMP certification requires ongoing professional development, ensuring that you remain up-to-date with the latest technologies and best practices in the industry. The CDMP covers below topics:

1. Data Management Process
2. Big Data
3. Data Architecture
4. Document and Content Management
5. Data Ethics
6. Data Governance
7. Data Integration and Interoperability
8. Master and Reference Data Management
9. Data Modelling and Design
10. Data Quality
11. Data Security
12. Data Storage and Operations
13. Data Warehousing and Business Intelligence
14. Metadata Management

The Certified Data Management Professional (CDMP) Data Quality Specialist exam is designed to assess a candidate’s knowledge and skills in data quality management. Here are some key aspects of the exam:

1. Data Profiling and Assessment: Understanding how to evaluate the quality of data by identifying anomalies, inconsistencies, and errors.
2. Data Cleansing Techniques: Mastery of various data cleansing methods, such as deduplication, standardization, and validation.
3. Data Quality Programs: Knowledge of developing and implementing data quality programs that align with business strategies.
4. Data Quality Tools: Ability to evaluate and select appropriate data quality tools and technologies for organizational needs.

The exam typically consists of multiple-choice questions that cover these areas, ensuring that candidates have a comprehensive understanding of data quality principles and practices.

Disclaimer: The sample questions provided are intended for practice purposes only and are not a substitute for the study material provided by the DAMA DMBOK. The questions are not guaranteed to be an accurate reflection of the actual exam questions. The use of these sample questions is entirely at your own risk, and we do not accept responsibility for any consequences resulting from their use. These sample questions are copyrighted and are not to be distributed or shared without prior permission. All copyrights are reserved.