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Distributed Data Processing Flashcards

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

Read the first 7 Distributed Data Processing flashcards as text

  1. Which delivery guarantee ensures each message is processed once with no duplicates or loss?

    Answer: Exactly-once

    Exactly-once semantics processes every record one time, avoiding duplicates and drops.

  2. In Kafka, what determines the maximum parallelism of consumers in a group?

    Answer: The number of partitions

    Each partition is consumed by at most one consumer in a group, capping parallelism.

  3. What is the main trade-off described by the CAP theorem during a network partition?

    Answer: Choosing between consistency and availability

    Under a partition you must sacrifice either consistency or availability.

  4. A windowed aggregation over event-time streams typically needs which mechanism to handle late data?

    Answer: Watermarks

    Watermarks define how long to wait for late events before finalizing a window.

  5. Why might increasing the number of partitions improve throughput up to a point?

    Answer: More partitions allow more parallel tasks

    More partitions enable greater parallelism, but too many add scheduling overhead.

  6. What is a coordinator's role in a two-phase commit protocol?

    Answer: Asking participants to prepare, then commit or abort

    The coordinator runs prepare and commit phases to keep a distributed transaction atomic.

  7. Which storage format is best suited for analytical column scans across distributed engines?

    Answer: Parquet

    Parquet's columnar layout lets engines read only needed columns efficiently.