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PDE Practice Question: Your team needs to store time-series data from…

Your team needs to store time-series data from millions of IoT devices. Each device sends a reading every 5 minutes, and the total data volume is about 2 TB per month. The most common query pattern is retrieving all readings for a specific device over a time range (e.g., last 24 hours). Which storage service should you choose?

⚠ Common exam trap

Google Cloud often tests the misconception that BigQuery is suitable for operational, low-latency time-series queries, but the trap here is that BigQuery is an analytical warehouse optimized for large-scale batch queries, not for repeated, sub-second per-device range scans, which is a classic NoSQL (Bigtable) workload.

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

✓

Cloud Bigtable

Cloud Bigtable is a fully managed, scalable NoSQL database designed for high-throughput, low-latency time-series data. It supports single-row key lookups and range scans, making it ideal for retrieving all readings for a specific device over a time range (e.g., last 24 hours) from millions of IoT devices generating 2 TB/month. Its row key design (e.g., device_id + timestamp) enables efficient time-range queries without full table scans, unlike object storage or analytical warehouses.

Answer analysis

Option-by-option breakdown

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

  • ✗

    Cloud Storage (objects per device per time interval)

    Why it's wrong here

    Cloud Storage objects cannot be queried by device and time range without listing and reading many objects, and per-interval object counts explode at millions of devices. A time-series database with device-and-timestamp indexing serves these range reads directly.

  • ✗

    BigQuery

    Why it's wrong here

    BigQuery is an analytical warehouse; scanning for one device's 24-hour readings across millions of devices incurs high per-query cost and latency without a clustered time-series key. A purpose-built time-series store with device and timestamp indexing answers these reads efficiently.

  • ✓

    Cloud Bigtable

    Why this is correct

    Bigtable's row-key design, keyed by device ID with time as a suffix, gives fast range scans for a single device over a time window, and scales horizontally for millions of devices writing every five minutes at 2 TB monthly.

  • ✗

    Cloud Spanner

    Why it's wrong here

    Cloud Spanner is a horizontally scalable relational database for transactional workloads, not optimised for high-ingest time-series range scans; its cost and schema model suit OLTP, not millions of device readings. A time-series database is the fit here.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.