Data for Leaders Awareness

  • 20m
  • 20 questions
The Data for Leaders Awareness benchmark will measure your ability to recall and relate to basic data concepts. You will be evaluated on your ability to recognize the foundational concepts of data such as data formats, sources, various data operations, terminologies, and processing methods. A learner who scores high on this benchmark demonstrates that they have a basic level of awareness of data concepts.

Topics covered

  • compare structured and unstructured data and describe how the ability to extract value from unstructured data is important when dealing with big data
  • define concepts essential to data science like dataset, database, data analytics, data aggregation, and time series
  • describe common sources of modern data and major data formats in use
  • describe common use cases and basic principles of data warehousing
  • describe extract, transform, and load (ETL) functionality and specify how the movement between transactional OLTP databases and a data warehouse is performed and how to organize and design your extraction, transformation, and loading capabilities to keep your data warehouse up-to-date
  • describe how a data warehouse is different from a database and how data warehouses are used for business intelligence
  • describe how to perform data migration and explain the functionality of common tools like extract, transform, and load (ETL)
  • describe online transaction processing in the context of relational databases and data warehousing
  • describe the difference between data warehousing and big data and specify the impact that big data has had on data warehousing
  • describe the difference between horizontal and vertical scaling and specify why horizontal scaling is the best choice with respect to big data
  • describe traditional data warehousing technologies such as virtual data warehousing and enterprise data warehousing
  • distinguish between raw data, information, applicable knowledge, and general wisdom
  • identify the sources that are capable of generating big data
  • list and describe the limitations of traditional data architecture, including limitations on speed, scalability, compatibility, and consumption
  • list and describe the limitations of using ETL systems when working with data, including limitations on performance, scalability, and structure
  • list the most commonly used data sources and formats
  • name and describe common database types used in the industry
  • name and describe the most commonly used ETL tools and software
  • outline disaster recovery plans and list common data backup strategies and tools
  • outline the evolution of data analytics, the changing perspectives with respect to it, and what's meant by descriptive, diagnostic, predictive, and prescriptive analytics

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