- A
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.
- B
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.
- C
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.
- D
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.
Question 296 of 1,000
Google ACE Planning and Configuring a Cloud Solution Practice Question
This ACE practice question tests your understanding of planning and configuring a cloud solution. 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 the most appropriate choice because it 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 read and write operations. Its key-value store with timestamp-based versioning is ideal for time-series IoT sensor data, and it supports millions of reads per second via its integration with Bigtable's HBase API and automatic sharding.
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.
- ✗
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.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that Cloud Spanner is the best choice for all high-throughput workloads due to its global scalability, but candidates fail to recognize that Spanner's relational model and strong consistency overhead make it unsuitable for the simple key-value, high-write-throughput pattern of time-series IoT data, where Bigtable's NoSQL design excels.
Detailed technical explanation
How to think about this question
Under the hood, Cloud Bigtable uses a distributed, sorted key-value map (similar to Google's internal Bigtable system) where rows are sorted by row key, and columns are grouped into column families; for time-series data, a common pattern is to use a row key like 'deviceID#reverseTimestamp' to enable efficient range scans and point lookups. Bigtable automatically splits and rebalances tablets across nodes based on load, and it leverages Colossus (Google's distributed file system) for storage, enabling seamless scaling to petabytes without manual sharding. A real-world scenario where this matters is a smart factory with millions of sensors generating data every second, where Bigtable can ingest and serve real-time dashboards with sub-10ms latency.
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.
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FAQ
Questions learners often ask
What does this ACE question test?
Planning and Configuring a Cloud Solution — This question tests Planning and Configuring a Cloud Solution — 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 the most appropriate choice because it 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 read and write operations. Its key-value store with timestamp-based versioning is ideal for time-series IoT sensor data, and it supports millions of reads per second via its integration with Bigtable's HBase API and automatic sharding.
What should I do if I get this ACE 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
This ACE 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 ACE exam.
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