CompTIA Data+ Mastery (Expert Level)

  • 40m
  • 40 questions
The CompTIA Data+ Mastery (Expert Level) benchmark measures your knowledge of data concepts and environments and applying data mining tasks and the appropriate descriptive statistical methods. You will be evaluated on your ability to summarize types of analysis and critical analysis techniques, create the appropriate visualization in a report or dashboard with proper design components, summarize important data governance concepts, and apply data quality control concepts. A learner who scores high on this benchmark demonstrates that they have expertise in data mining, analysis, and visualization and have the necessary skills to facilitate data-driven business decisions.

Topics covered

  • apply style guides to reports
  • create users, groups, and roles to grant access to data
  • define MDM and its purpose
  • describe data manipulation best practices
  • describe key components of data usage management
  • describe performance analysis, including how to track measurements against defined goals and use basic projections to achieve goals
  • describe the purpose of role-based access control (RBAC) in data analytics
  • describe the role of data de-identification and masking
  • describe the role of data quality dimensions
  • describe the role of regulatory requirements in data analytics
  • describe various processes used in MDM
  • identify corporate standards and branding elements to be implemented in reports
  • identify examples of good data and poor data
  • identify the elements and requirements for the release process when dealing with data
  • identify the need for access requirements in data analytics
  • identify the purpose of entity relationship requirements in data analytics
  • implement filters in a dashboard
  • implement various report elements
  • measure confidence intervals using a dataset
  • optimize dashboards for better performance
  • outline data validation activities and techniques
  • outline query optimization best practices
  • outline recurring and tactical reports
  • outline storage environment requirements in data analytics
  • outline the need for data classification in data analytics
  • outline the need for data protection in data analytics
  • outline the purposes of MicroStrategy and BusinessObjects and the roles they play in data analytics
  • outline the steps for data breach reporting
  • outline various validation methods
  • outline various ways to measure data quality
  • perform correlation testing using a dataset
  • provide an overview of different types of data validation
  • provide an overview of scoping and gap analysis as key analytic techniques
  • provide an overview of trend analysis as a comparison of data over time
  • provide an overview of use requirements in data analytics
  • set up geographic map charts by working with a chart creation program and data
  • use execution plans
  • use record subsets with a dataset
  • use statistics and chart software to make treemap charts
  • utilize access permissions in a dashboard