- A
Firestore
Why wrong: Firestore is a document database suited for mobile/web apps with hierarchical data. It does not scale to millions of QPS for time-series workloads.
- B
Cloud Spanner
Why wrong: Spanner is a globally distributed relational database optimised for ACID transactions, not single-digit ms time-series reads at millions of QPS.
- C
Cloud Bigtable
Bigtable is the correct choice: wide-column NoSQL, designed for time-series and IoT workloads, single-digit ms latency, and scales to millions of QPS with additional nodes.
- D
BigQuery
Why wrong: BigQuery is an analytics data warehouse with query latency measured in seconds. It is not designed for millisecond lookups at IoT scale.
Question 439 of 1,000
PDE Storing the Data Practice Question
This PDE practice question tests your understanding of storing the data. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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 large analytical and operational workloads, handling petabytes of data with consistent single-digit millisecond latency for high-throughput reads and writes. Its key-value model with timestamp-based versioning is ideal for time-series IoT sensor data, and it supports millions of reads per second via its HBase API and Bigtable's underlying tablet-based architecture.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 suited for mobile/web apps with hierarchical data. It does not scale to millions of QPS for time-series workloads.
- ✗
Cloud Spanner
Why it's wrong here
Spanner is a globally distributed relational database optimised for ACID transactions, not single-digit ms time-series reads at millions of QPS.
- ✓
Cloud Bigtable
Why this is correct
Bigtable is the correct choice: wide-column NoSQL, designed for time-series and IoT workloads, single-digit ms latency, and scales to millions of QPS with additional nodes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
BigQuery
Why it's wrong here
BigQuery is an analytics data warehouse with query latency measured in seconds. It is not designed for millisecond lookups at IoT scale.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common trap in Google exams is to choose Cloud Spanner for structured data, but Spanner is optimized for strong consistency and transactional workloads, not for high-throughput time-series data at petabyte scale with millions of reads per second. BigQuery is for analytical queries, not real-time key-value access. Bigtable's key-value model and low-latency high-throughput design make it the correct choice.
Detailed technical explanation
How to think about this question
Cloud Bigtable uses a distributed, sorted key-value map (based on Google's Bigtable paper) where rows are sorted lexicographically by row key, enabling efficient range scans and point lookups. For time-series data, a common pattern is to use a row key that combines a sensor ID with a reversed timestamp to avoid hot-spotting and ensure even load distribution across tablets. Bigtable's replication across zones and automatic sharding allow it to sustain millions of reads per second with sub-10ms p99 latency, making it the standard choice for IoT, ad tech, and financial time-series workloads.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this PDE question test?
Storing the Data — This question tests Storing the Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Cloud Bigtable — Cloud Bigtable is a fully managed, scalable NoSQL database designed for large analytical and operational workloads, handling petabytes of data with consistent single-digit millisecond latency for high-throughput reads and writes. Its key-value model with timestamp-based versioning is ideal for time-series IoT sensor data, and it supports millions of reads per second via its HBase API and Bigtable's underlying tablet-based architecture.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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