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CTP Data Management & Analytics Flashcards

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

Read the first 6 CTP Data Management & Analytics flashcards as text
  1. Which data modeling technique is most appropriate for designing a relational database to support complex reporting requirements in an enterprise system?

    Answer: Dimensional modeling with star schema

    Dimensional modeling with a star schema separates facts from dimensions, making it optimal for complex enterprise reporting queries.

  2. What is the primary purpose of data normalization in relational database design?

    Answer: To reduce data redundancy and improve data integrity

    Normalization eliminates redundant data and organizes columns and tables to ensure data dependencies make logical sense, improving integrity.

  3. A CTP candidate is evaluating ETL pipeline performance. Which metric best indicates pipeline throughput?

    Answer: Records processed per second

    Records processed per second directly measures how efficiently the ETL pipeline moves and transforms data over time.

  4. Which approach best handles slowly changing dimensions (SCD) in a data warehouse?

    Answer: SCD Type 2: adding new rows with effective date ranges

    SCD Type 2 preserves full history by inserting new rows with start/end dates, enabling point-in-time historical analysis.

  5. What does ACID stand for in the context of database transactions?

    Answer: Atomicity, Consistency, Isolation, Durability

    ACID properties—Atomicity, Consistency, Isolation, Durability—guarantee reliable database transactions even in the event of errors or failures.

  6. A technical professional must choose between OLTP and OLAP systems. Which system is best suited for complex analytical queries across large historical datasets?

    Answer: OLAP (Online Analytical Processing)

    OLAP systems are optimized for read-heavy analytical queries, aggregations, and historical data analysis across large datasets.