The answer is to add more nodes to the instance and ensure read requests are distributed evenly. This is correct because in Spanner’s shared-nothing architecture, high CPU utilization on some nodes signals a hot spot where read traffic is concentrated, causing increased read latency. Adding nodes scales out compute capacity, while ensuring even load distribution—through proper key design or Spanner’s built-in load balancing—prevents those hot spots and reduces latency spikes. On the Google Professional Cloud DevOps Engineer exam, this scenario tests your understanding of Spanner’s horizontal scaling and the critical difference between simply adding capacity and actually distributing the workload. A common trap is to focus on caching or write optimization, but for a read-heavy workload with uneven CPU, the bottleneck is node-level contention, not throughput. Remember the mnemonic: “Hot nodes need more nodes and even loads.”
PCDOE Optimizing service performance Practice Question
This PCDOE practice question tests your understanding of optimizing service performance. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 team uses Spanner for a global database. They notice increased read latency and high CPU utilization on some nodes. The workload is read-heavy with occasional writes. Which action is most likely to improve performance?
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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add more nodes to the instance and ensure read requests are distributed evenly.
In a read-heavy Spanner workload with high CPU utilization on some nodes, adding more nodes and ensuring read requests are distributed evenly (Option D) directly addresses the bottleneck by increasing the instance's compute capacity and spreading the load across all nodes. Spanner's architecture uses a shared-nothing design where each node handles a portion of the data and traffic; uneven distribution can cause hot spots. Adding nodes scales out processing power, and ensuring even distribution (e.g., via proper key design or using Spanner's built-in load balancing) reduces latency and CPU spikes on individual nodes.
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.
✗
Create read-only replicas in each region.
Why it's wrong here
Spanner doesn't have read-only replicas; all replicas can serve reads.
✗
Split the most frequently read tables into smaller tables.
Why it's wrong here
Splitting may increase cross-node joins.
✗
Increase the number of nodes in the instance.
Why it's wrong here
More nodes add CPU but also increase write latency due to quorum.
✓
Add more nodes to the instance and ensure read requests are distributed evenly.
Why this is correct
More nodes spread read load and reduce CPU per node.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that adding nodes alone (Option C) solves performance issues, but the trap is that without even distribution of read requests, hot spots persist, making Option D the only complete solution.
Detailed technical explanation
How to think about this question
Spanner uses a distributed, shared-nothing architecture where each node manages a set of splits (tablets). When read requests are skewed (e.g., due to a monotonically increasing key), a single node can become a hot spot, causing high CPU and latency. Adding nodes and distributing reads evenly (e.g., by using hash-based sharding or Spanner's interleaved tables) allows the load to be spread across more splits, reducing per-node CPU. Spanner's automatic split management can rebalance data over time, but manual intervention (like key design) is often needed for immediate relief.
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.
Optimizing service performance — This question tests Optimizing service performance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Add more nodes to the instance and ensure read requests are distributed evenly. — In a read-heavy Spanner workload with high CPU utilization on some nodes, adding more nodes and ensuring read requests are distributed evenly (Option D) directly addresses the bottleneck by increasing the instance's compute capacity and spreading the load across all nodes. Spanner's architecture uses a shared-nothing design where each node handles a portion of the data and traffic; uneven distribution can cause hot spots. Adding nodes scales out processing power, and ensuring even distribution (e.g., via proper key design or using Spanner's built-in load balancing) reduces latency and CPU spikes on individual nodes.
What should I do if I get this PCDOE 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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Question Discussion
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