- A
Cloud Firestore
Why wrong: Firestore is a document database for app backends — it doesn't handle 500K writes/second or provide the columnar storage needed for time-series analytics at this scale.
- B
Cloud SQL for MySQL
Why wrong: Cloud SQL is a relational database with limited write throughput — it cannot handle 500K events/second ingestion.
- C
Cloud Bigtable
Bigtable is purpose-built for high-throughput, low-latency NoSQL workloads including IoT time-series. It scales linearly with node count and supports the ingestion rate and query patterns described.
- D
BigQuery streaming inserts
Why wrong: BigQuery supports streaming inserts but has throughput limits and latency unsuitable for 500K events/second real-time ingestion. It's better as an analytics sink after buffering.
Quick Answer
The answer is Cloud Bigtable, the most appropriate service for storing and querying IoT sensor data at 500,000 events per second for time-series analytics. This is correct because Cloud Bigtable is a fully managed, scalable NoSQL database built for high-throughput, low-latency workloads, using a wide-column storage model that excels at ingesting massive event streams and performing efficient range scans across billions of rows—exactly what time-series analytics queries require. On the Google Associate Cloud Engineer exam, this question tests your ability to match workload characteristics to storage services; a common trap is choosing BigQuery for storage alone, but Bigtable handles real-time ingestion while BigQuery is better for ad-hoc analytics on top of that data. Remember the memory tip: “Bigtable for big throughput, BigQuery for big queries”—if you need to write fast and scan wide, think Bigtable.
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 media company ingests 500,000 events per second from IoT sensors and needs to store them for time-series analytics queries that scan billions of rows. Which storage service 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 service because it is a fully managed, scalable NoSQL database designed for high-throughput, low-latency workloads like IoT sensor data ingestion at 500,000 events per second. It supports time-series analytics queries scanning billions of rows via its wide-column storage model and integration with BigQuery for complex analytics, while providing sub-10ms latency for point lookups and efficient range scans.
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 Firestore
Why it's wrong here
Firestore is a document database for app backends — it doesn't handle 500K writes/second or provide the columnar storage needed for time-series analytics at this scale.
- ✗
Cloud SQL for MySQL
Why it's wrong here
Cloud SQL is a relational database with limited write throughput — it cannot handle 500K events/second ingestion.
- ✓
Cloud Bigtable
Why this is correct
Bigtable is purpose-built for high-throughput, low-latency NoSQL workloads including IoT time-series. It scales linearly with node count and supports the ingestion rate and query patterns described.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
BigQuery streaming inserts
Why it's wrong here
BigQuery supports streaming inserts but has throughput limits and latency unsuitable for 500K events/second real-time ingestion. It's better as an analytics sink after buffering.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that BigQuery streaming inserts are a storage service for high-ingestion workloads, but the trap here is that BigQuery is a data warehouse for analytics, not a low-latency storage system for time-series data, and its streaming limit is far lower than Bigtable's throughput.
Detailed technical explanation
How to think about this question
Cloud Bigtable uses a distributed, sharded architecture based on Google's Bigtable paper, where data is sorted by row key and stored in tablets; for time-series data, a common design pattern uses a row key like 'sensorID#timestamp' to enable efficient range scans over billions of rows. Under the hood, Bigtable leverages Colossus (Google's distributed file system) for storage and provides a HBase-compatible API, allowing direct integration with open-source tools like Apache Spark and Hadoop for complex analytics. A real-world scenario is a smart city project ingesting traffic sensor data at 1 million events per second, where Bigtable handles the write load and enables queries like 'average speed per sensor over the last hour' via row key prefix scans.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 service because it is a fully managed, scalable NoSQL database designed for high-throughput, low-latency workloads like IoT sensor data ingestion at 500,000 events per second. It supports time-series analytics queries scanning billions of rows via its wide-column storage model and integration with BigQuery for complex analytics, while providing sub-10ms latency for point lookups and efficient range scans.
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.
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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: Jun 30, 2026
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