- A
Insufficient compute capacity (nodes) allocated to the instance.
Why wrong: While compute can be an issue, 50% latency increase typically indicates structural contention rather than capacity.
- B
Hotspotting due to monotonically increasing primary keys.
This is a common cause of latency spikes in Spanner; use hash-prefixed keys to distribute writes.
- C
Incorrect indexing of secondary indexes.
Why wrong: Indexing affects read performance, not write commit latency.
- D
Network bandwidth constraints between regions.
Why wrong: Network latency may contribute but is less likely to cause such a sharp increase during peak hours without key contention.
Question 43 of 1,000
PDE Ensuring solution quality Practice Question
This PDE practice question tests your understanding of ensuring solution quality. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 uses Cloud Spanner for a global transactional application. During peak hours, commit latency increases by over 50%. Which configuration issue is the most likely root cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
Hotspotting due to monotonically increasing primary keys.
In Cloud Spanner, monotonically increasing primary keys (e.g., sequential integers or timestamps) cause all writes to be directed to a single split, creating a hotspot. This leads to contention and increased commit latency during peak loads, as the single node handling that split becomes a bottleneck. Option B correctly identifies this as the most likely root cause.
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.
- ✗
Insufficient compute capacity (nodes) allocated to the instance.
Why it's wrong here
While compute can be an issue, 50% latency increase typically indicates structural contention rather than capacity.
- ✓
Hotspotting due to monotonically increasing primary keys.
Why this is correct
This is a common cause of latency spikes in Spanner; use hash-prefixed keys to distribute writes.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Incorrect indexing of secondary indexes.
Why it's wrong here
Indexing affects read performance, not write commit latency.
- ✗
Network bandwidth constraints between regions.
Why it's wrong here
Network latency may contribute but is less likely to cause such a sharp increase during peak hours without key contention.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that scaling nodes (Option A) is the universal fix for latency issues, but the trap here is that hotspotting from monotonically increasing keys is a specific, common cause of commit latency spikes in Cloud Spanner that cannot be resolved by adding nodes alone.
Detailed technical explanation
How to think about this question
Cloud Spanner uses a distributed, strongly consistent storage system that splits data into tablets based on primary key ranges. When a primary key is monotonically increasing (e.g., auto-increment IDs), all new writes land on the last tablet, causing that tablet’s leader to handle all write traffic and become a hot spot. This can be mitigated by using key hashing, UUIDs, or bit-reversed sequential keys to distribute writes evenly across splits.
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 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.
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?
Ensuring solution quality — This question tests Ensuring solution quality — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Hotspotting due to monotonically increasing primary keys. — In Cloud Spanner, monotonically increasing primary keys (e.g., sequential integers or timestamps) cause all writes to be directed to a single split, creating a hotspot. This leads to contention and increased commit latency during peak loads, as the single node handling that split becomes a bottleneck. Option B correctly identifies this as the most likely root cause.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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