Quick Answer
The answer is CPU utilization per node. This metric is the first to examine because Cloud Spanner operates on a shared-nothing architecture where each node independently processes its assigned data and queries; when point read latency is high despite an evenly distributed workload, it typically indicates that individual nodes are hitting CPU saturation, causing queuing and delayed responses. On the Google Professional Cloud Database Engineer exam, this question tests your understanding that even distribution does not guarantee per-node efficiency—a common trap is to blame network or storage metrics first, when the bottleneck is actually processing capacity. Remember the mnemonic “CPU per node, not per cluster” to avoid overlooking node-level saturation as the root cause of high point read latency.
This PCDE practice question tests your understanding of monitor and optimize database 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.
Your Cloud Spanner instance has high latency for point reads. The workload is evenly distributed across all nodes. Which metric should you examine first to identify the bottleneck?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
CPU utilization per node
High CPU suggests node is overloaded, causing latency.
Storage utilization
Why wrong: Storage doesn't impact point read latency directly.
Rows returned per second
Why wrong: This measures throughput, not latency.
Number of committed nodes
Why wrong: Committed nodes is about reservations, not current performance.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
CPU utilization per node
High latency for point reads in Cloud Spanner, even with evenly distributed workload, often points to CPU saturation on individual nodes. Spanner uses a shared-nothing architecture where each node handles a portion of the data and queries; if CPU utilization per node is high, it indicates that the node is overloaded, causing queuing and increased latency. This metric directly reflects processing capacity and is the first place to look for a bottleneck in point-read performance.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
CPU utilization per node
Why this is correct
High CPU suggests node is overloaded, causing latency.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Storage utilization
Why it's wrong here
Storage doesn't impact point read latency directly.
Rows returned per second
Why it's wrong here
This measures throughput, not latency.
Number of committed nodes
Why it's wrong here
Committed nodes is about reservations, not current performance.
Common exam traps
Google Cloud often tests the misconception that evenly distributed workload means no node-level bottleneck, but the trap here is that even distribution does not guarantee low latency if each node is individually under high CPU load, so candidates incorrectly focus on throughput or storage metrics instead of CPU utilization.
Detailed technical explanation
Under the hood, Cloud Spanner splits data into splits (tablets) distributed across nodes; each node has its own CPU, memory, and storage. Point reads are routed to the node holding the relevant split, and if that node's CPU is saturated (e.g., above 80% sustained), read requests queue up, increasing latency. In practice, even with even load distribution, hot spots can occur due to key access patterns or split boundaries, making per-node CPU the most granular indicator of resource contention.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Monitor and optimize database performance — This question tests Monitor and optimize database performance — Read the scenario before looking for a memorised answer..
The correct answer is: CPU utilization per node — High latency for point reads in Cloud Spanner, even with evenly distributed workload, often points to CPU saturation on individual nodes. Spanner uses a shared-nothing architecture where each node handles a portion of the data and queries; if CPU utilization per node is high, it indicates that the node is overloaded, causing queuing and increased latency. This metric directly reflects processing capacity and is the first place to look for a bottleneck in point-read performance.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This PCDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PCDE exam.
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