The answer is to increase the number of Cloud Spanner nodes to 150. This directly resolves DEADLINE_EXCEEDED errors and high write latency by adding more compute and storage capacity, which reduces CPU utilization from its 80% spike and distributes the 10,000 writes per second more evenly across the instance. With average commit latency at 50ms and CPU hovering in the 60-80% range, the instance is nearing its throughput ceiling; scaling nodes alleviates the bottleneck without requiring schema or application changes, since hot keys and hash prefixes are already optimized. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding that Spanner’s horizontal scaling is the primary lever for write throughput—not tuning queries or splitting tables. A common trap is to overcomplicate the fix with schema redesign when the metrics clearly point to capacity limits. Memory tip: “CPU at 80%? Time to elevate—add nodes, not complexity.”
PCDE Monitor and optimize database performance 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. 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.
Your company runs a global gaming platform using Cloud Spanner as the backend database. The platform has millions of users who play concurrently. You receive reports that during peak hours (7-10 PM UTC), some users experience 'DEADLINE_EXCEEDED' errors and high latency on write operations. You have already verified that there are no hot keys and that the schema uses primary keys with hash prefixes. Monitoring shows CPU utilization averages 60% but spikes to 80% during the peak. The average commit latency is 50ms during peak, and the transaction rate is 10,000 writes per second. The instance currently has 100 nodes. The application team indicates that writes are primarily player score updates. What should you do to resolve the performance issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "primary"
Why it matters: Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the number of Spanner nodes to 150.
Option C is correct because increasing the number of Spanner nodes from 100 to 150 directly adds more compute and storage capacity, reducing CPU utilization from the 80% spike and lowering write latency. The 60-80% CPU range with 50ms commit latency indicates the instance is nearing its throughput limit, and adding nodes distributes the write load (10,000 writes/sec) more evenly, alleviating 'DEADLINE_EXCEEDED' errors without requiring schema or application changes.
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.
✗
Enable Fine-Grained Latency & Replication (FLLR) to improve write latency.
Why it's wrong here
FLLR is for replicating data to multiple regions; it doesn't reduce CPU utilization.
✗
Disable client-side buffering for write operations.
Why it's wrong here
Client-side buffering is optional and disabling it could increase network round trips.
✓
Increase the number of Spanner nodes to 150.
Why this is correct
More nodes add capacity, reducing CPU pressure and latency.
Clue confirmation
The clue word "primary" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce the size of write transactions by batching fewer mutations per transaction.
Why it's wrong here
Transaction size might be a factor, but with 50ms commit latency, the bottleneck is CPU, not mutation size.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that reducing transaction size or disabling buffering always improves performance, but in Spanner, CPU saturation from high write throughput is best resolved by horizontal scaling (adding nodes), not by reducing batch sizes or tweaking client settings.
Detailed technical explanation
How to think about this question
Cloud Spanner uses a shared-nothing architecture where each node provides a fixed amount of compute (vCPU) and storage capacity; at 100 nodes, the instance can handle up to 10,000 write QPS with 2-10ms typical commit latency, but 50ms indicates queuing due to CPU saturation. Increasing nodes to 150 adds 50% more processing power, reducing the CPU utilization below 60% and allowing writes to complete faster, as Spanner automatically redistributes splits across the new nodes. In practice, this is a common scaling pattern for gaming platforms with predictable peak loads, where vertical scaling (more nodes) is preferred over application-level changes when CPU is the bottleneck.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Monitor and optimize database performance — This question tests Monitor and optimize database performance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the number of Spanner nodes to 150. — Option C is correct because increasing the number of Spanner nodes from 100 to 150 directly adds more compute and storage capacity, reducing CPU utilization from the 80% spike and lowering write latency. The 60-80% CPU range with 50ms commit latency indicates the instance is nearing its throughput limit, and adding nodes distributes the write load (10,000 writes/sec) more evenly, alleviating 'DEADLINE_EXCEEDED' errors without requiring schema or application changes.
What should I do if I get this PCDE 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: "primary". Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
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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