- A
Reduce the batch size of writes to decrease the load on each node.
Why wrong: Smaller batches won't fix the uneven distribution of keys; the hot node will still receive more writes.
- B
Use a different Bigtable cluster and migrate data.
Why wrong: A new cluster with the same row key design will still have the same issue.
- C
Increase the number of nodes in the cluster to provide more CPU capacity.
Why wrong: More nodes may temporarily reduce pressure on overloaded nodes, but the hot spotting will persist because the row key design is sequential.
- D
Prepend a hash of the sensor ID to the row key to distribute writes evenly.
This breaks the sequential key pattern and distributes writes across all nodes, eliminating hot spotting.
Question 217 of 1,000
PCDE Row key design Practice Question
This PCDE practice question tests your understanding of monitor and optimize database performance. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: row key design. 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.
Your team uses Cloud Bigtable for a time-series data analytics platform. You observe that the write throughput has dropped significantly, and Cloud Monitoring shows that most of the CPU usage is concentrated on a few nodes. The remaining nodes have low CPU usage. The data model uses sequential timestamps as row keys, and the application writes data for many different sensors. Each sensor ID is part of the row key. What is the most effective action to resolve this hot spotting?
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
✓
Prepend a hash of the sensor ID to the row key to distribute writes evenly.
Hot spotting occurs because sequential timestamps as row keys cause writes to be concentrated on a few nodes. By prepending a hash of the sensor ID to the row key, writes for different sensors are distributed across the entire cluster, eliminating hot spots. Option A (reducing batch size) does not fix the key design issue. Option B (increasing nodes) might help temporarily but the uneven distribution remains. Option C (using a different cluster) is unnecessary and does not address the root cause.
Key principle: Row key design
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the batch size of writes to decrease the load on each node.
Why it's wrong here
Smaller batches won't fix the uneven distribution of keys; the hot node will still receive more writes.
- ✗
Use a different Bigtable cluster and migrate data.
Why it's wrong here
A new cluster with the same row key design will still have the same issue.
- ✗
Increase the number of nodes in the cluster to provide more CPU capacity.
Why it's wrong here
More nodes may temporarily reduce pressure on overloaded nodes, but the hot spotting will persist because the row key design is sequential.
- ✓
Prepend a hash of the sensor ID to the row key to distribute writes evenly.
Why this is correct
This breaks the sequential key pattern and distributes writes across all nodes, eliminating hot spotting.
Related concept
Row key design
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Row key design
- Hot spotting
- Hashing prefix
- Cloud Bigtable
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
Row key design
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
Visual reference
What to study next
Got this wrong? Here's your next step.
Review row key design, then practise related PCDE questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this PCDE question test?
Monitor and optimize database performance — This question tests Monitor and optimize database performance — Row key design.
What is the correct answer to this question?
The correct answer is: Prepend a hash of the sensor ID to the row key to distribute writes evenly. — Hot spotting occurs because sequential timestamps as row keys cause writes to be concentrated on a few nodes. By prepending a hash of the sensor ID to the row key, writes for different sensors are distributed across the entire cluster, eliminating hot spots. Option A (reducing batch size) does not fix the key design issue. Option B (increasing nodes) might help temporarily but the uneven distribution remains. Option C (using a different cluster) is unnecessary and does not address the root cause.
What should I do if I get this PCDE question wrong?
Review row key design, then practise related PCDE questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Row key design
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Last reviewed: Jun 24, 2026
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