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Google PCA Practice Question: Store petabytes of time-series IoT sensor data…
A company needs to store petabytes of time-series IoT sensor data and query it with single-digit millisecond latency at millions of reads per second. The data has a simple key-value structure with timestamps. Which Google Cloud database is MOST appropriate?
⚠ Common exam trap
PCA often tests the distinction between Bigtable (low-latency, high-throughput NoSQL) and BigQuery (analytics warehouse) — candidates must match the latency and throughput requirements to the right service.
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 Google's petabyte-scale, low-latency NoSQL wide-column store, purpose-built for time-series and IoT workloads with single-digit millisecond latency at millions of reads/writes per second. Its row-key design supports efficient range scans by timestamp, and it scales horizontally by adding nodes, making it the best fit for high-throughput sensor data with simple key-value access.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Firestore
Why it's wrong here
Firestore is a document database with per-document read latency in the tens of milliseconds and no native time-series key-range aggregation, so it cannot meet single-digit millisecond reads at millions per second. It suits mobile and web app sync with hierarchical documents, not petabyte sensor ingestion.
- ✓
Cloud Bigtable
Why this is correct
Cloud Bigtable is a wide-column NoSQL store designed for petabyte-scale time-series data, delivering consistent single-digit millisecond latency at millions of reads per second. Its sparse key-value structure with timestamp row keys matches the schema described.
- ✗
Cloud Spanner
Why it's wrong here
Cloud Spanner is a relational database whose latency and cost target transactional workloads, not millions of key-value reads per second across petabytes. It is tempting because it scales horizontally with strong consistency, which suits globally distributed OLTP applications rather than high-throughput time-series ingestion and lookup.
- ✗
BigQuery
Why it's wrong here
BigQuery is an analytical warehouse optimised for large scans and aggregations, not single-digit millisecond point reads at millions per second. It is tempting because it stores petabytes cheaply and queries time-series data with SQL, which suits batch analytics and reporting rather than low-latency key-value serving.
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Related to this question
Learn chapter
Security Best Practices and Compliance
Key term
Latency
Latency is the time delay between a request being sent over a network and the response being received, often measured in milliseconds.
Key term
Throughput
Throughput is the rate at which data is successfully transferred from one point to another over a network, typically measured in bits per second.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PCA 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 PCA exam.