How to Study for the Certified Data Management Professional (CDMP) Exam

Study Tips

💌 Sign up for the CDMP Study Plan.  This will help you maximize your time and energy while preparing for the test.  Each week covers a different chapter or chapters of the DMBOK with key takeaways, vocabulary, and study guides you can print and use during the test.  The study schedule leverages the 80/20 rule to keep you focused on the most important content likely to appear on the exam.  And because certification is about more than getting top marks on the exam, the study plan includes thoughtful external resources, additional reading, and preparation for a career as a data professional.

Study Topics

The CDMP covers 14 topics of the DMBOK —I’ve listed them in order of the prevalence with which they occur on the exam and provided a brief definition for each.

Data Governance (11%) — practices and processes to ensure formal management of data assets. Read more.

Data Quality (11%) — assuring data is fit for consumption based on its accuracy, completeness, consistency, integrity, reasonability, timeliness, uniqueness/deduplication, validity, and accessibility. Read more.

Data Modeling and Design (11%) — translation of business needs into technical specifications. Read more.

Metadata Management (11%) — information about data collected. Read more.

Master and Reference Data Management (10%) — reference data is information used to categorize other data found in a database, or information that is solely for relating data in a database to information beyond the boundaries of the organization. Master reference data refers to information that is shared across a number of systems within the organization. Read more.

Data Warehousing and Business Intelligence (10%) — a data warehouse stores information from operational systems (as well as other data resources, potentially) in a way that is optimized to support decision-making processes. Business intelligence refers to the use of technology to gather and analyze data, then translate it into useful information. Read more.

Document and Content Management (6%) — technologies, methods, and tools used to organize and store an organization’s documents. Read more.

Data Integration and Interoperability (6%) — use of technical and business processes to merge data from different sources, with the goal of readily and efficiently providing access to valuable information. Read more.

Data Architecture (6%) — specifications to describe existing state, define data requirements, guide data integration, and control data assets, according to the organization’s data strategy. Read more.

Data Security (6%) — implementation of policies and procedures to ensure people and things take the right actions with data and information assets, even in the presence of malicious inputs. Read more.

Data Storage and Operations (6%) — characterization of hardware or software that holds, deletes, backs up, organizes, and secures an organization’s information. Read more.

Data Management Process (2%) — end-to-end management of data, including collection, control, protection, delivery, and enhancement. Read more.

Big Data (2%) — extremely large datasets, often composed of various structured, unstructured, and semi-structured data types. Read more.

Data Ethics (2%) — code of conduct encompassing data handling, algorithms, and other practices to ensure that data is used appropriately in a moral context. Read more.

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