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System Architecture & Design Flashcards

7 cards from real CDO practice questions. Tap to flip, then mark Knew It or Still Learning โ€” missed cards come back until you master them.

Read the first 7 System Architecture & Design flashcards as text
  1. In a medallion lakehouse architecture, which layer typically holds cleansed, conformed, and deduplicated data that is not yet aggregated for business reporting?

    Answer: Silver

    Bronze holds raw data, silver holds cleansed and conformed data, and gold holds business-level aggregates.

  2. A data lake applies structure only when data is queried, not when it is loaded. What is this approach called?

    Answer: Schema-on-read

    Schema-on-read stores raw data and interprets its structure at query time, which gives flexibility at ingestion.

  3. Which slowly changing dimension type keeps full history by adding a new row with effective dates each time an attribute changes?

    Answer: Type 2

    SCD Type 2 adds a new versioned row for every change and keeps all earlier rows for historical analysis.

  4. Why are columnar file formats such as Parquet preferred for analytical workloads?

    Answer: They allow column pruning and high compression for scan-heavy queries

    Columnar storage reads only the needed columns and compresses similar values well, which speeds up analytics.

  5. How does a snowflake schema differ from a star schema?

    Answer: Its dimension tables are normalized into multiple related tables

    A snowflake schema normalizes dimensions into sub-dimensions, which reduces redundancy but adds joins.

  6. Which architecture processes all data, both real-time and historical, through a single stream-processing path and reprocesses history by replaying the log?

    Answer: Kappa architecture

    Kappa removes Lambda's separate batch layer and treats everything as a replayable stream.

  7. A table of billions of event records is mostly queried by date range. Which physical design choice improves query performance the most?

    Answer: Partitioning the table by event date

    Partitioning by date lets the engine skip partitions outside the queried range (partition pruning).

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