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PCDE Practice Question: Design innovative, scalable, and highly available cloud database solutions

A team is designing a Bigtable schema for a real-time fraud detection system. The row key includes device ID and timestamp. They need to avoid hotspotting during high write periods. Which two row key design patterns help achieve this? (Choose TWO.)

⚠ Common exam trap

Google Cloud often tests the distinction between patterns that optimize reads (like putting filtered fields first) versus patterns that prevent write hotspotting (like salting or hashing), and candidates mistakenly choose read-optimization patterns for a write-heavy scenario.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Add a hash prefix or salt to the beginning of the row key.

Adding a hash prefix or salt to the beginning of the row key distributes writes across multiple tablet servers, preventing hotspotting on a single node. In Bigtable, row keys are sorted lexicographically, so sequential device IDs or timestamps would cause all writes to hit the same tablet. A hash prefix ensures even distribution of write load.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Add a hash prefix or salt to the beginning of the row key.

    Why this is correct

    Salting distributes writes across tablets.

  • Put the most frequently filtered fields first in the row key.

    Why it's wrong here

    That optimizes reads, not write distribution.

  • Use reverse timestamp to keep recent data first.

    Why it's wrong here

    Doesn't prevent hotspotting; can even cause it if many writes happen at the same time.

  • Use device ID as the sole row key prefix to group data by device.

    Why it's wrong here

    High-volume devices cause hotspots.

  • Use a hash of the device ID to randomize the row key.

    Why this is correct

    Hashing ensures even distribution.

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Last reviewed: Jul 4, 2026

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