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DAC Business Intelligence & Analytics Strategy Flashcards

6 cards from real DAC practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

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  1. What is ETL in data analytics?

    Answer: Extract, Transform, Load — the process of moving data from sources into a target data store

    ETL stands for Extract, Transform, Load — the pipeline process of pulling data from source systems, transforming it for analysis, and loading it into a target data warehouse.

  2. Which of the following best describes a data lake?

    Answer: A repository that stores vast amounts of raw data in its native format until needed

    A data lake stores large volumes of raw, unstructured, semi-structured, and structured data in its native format, offering flexible schema-on-read capabilities.

  3. What is the difference between OLTP and OLAP systems?

    Answer: OLTP supports high-volume transactional operations; OLAP is optimized for complex analytical and historical queries

    OLTP (Online Transaction Processing) handles frequent, short operational transactions, while OLAP (Online Analytical Processing) is designed for complex queries over large historical datasets.

  4. In a balanced scorecard approach to analytics strategy, which of the following is NOT one of the four perspectives?

    Answer: Technology Infrastructure

    The Balanced Scorecard's four perspectives are Financial, Customer, Internal Business Processes, and Learning & Growth — technology infrastructure is not one of the four.

  5. What is self-service BI?

    Answer: An approach allowing business users to access, analyze, and visualize data without relying on IT or data teams

    Self-service BI empowers non-technical business users to independently explore data, create reports, and derive insights using intuitive tools without IT dependency.

  6. What is data-driven decision making?

    Answer: Using analysis of data and metrics to guide business decisions rather than relying solely on intuition

    Data-driven decision making uses analysis of collected data and relevant metrics to inform and validate business decisions, reducing reliance on intuition alone.