Quick Answer
The answer is to check the lock conflicts metric, specifically Spanner/API/Lock_wait. This is correct because high commit latency in Cloud Spanner, especially for single-row inserts, is most commonly caused by lock contention where concurrent transactions compete for the same row or index partition, forcing transactions to wait before committing. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding of Spanner’s distributed locking model and the distinction between lock wait time and other latency sources like CPU or network. A common trap is to immediately suspect storage or throughput issues, but for write-heavy single-row workloads, lock conflicts are the primary culprit. Remember the memory tip: “Single-row inserts + high commit latency = lock wait first,” as this metric directly measures the time spent waiting for locks, isolating the root cause before investigating other factors.
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.
You are monitoring a Cloud Spanner instance and see that the average commit latency is high. The application performs many single-row inserts. Which metric would you check first to understand the root cause?
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.
Read latency.
Why wrong: Read latency is separate from write commit latency.
Lock conflicts (e.g., Spanner/API/Lock_wait).
Lock conflicts directly indicate contention causing commit delays.
Storage utilization.
Why wrong: Storage utilization is capacity-related, not a direct cause of commit latency.
CPU utilization per node.
Why wrong: CPU may be high but does not pinpoint contention.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Lock conflicts (e.g., Spanner/API/Lock_wait).
High commit latency for single-row inserts in Cloud Spanner is often caused by lock conflicts, as concurrent transactions may contend for the same row or index. The metric Spanner/API/Lock_wait directly measures time spent waiting for locks, making it the first metric to check. High commit latency without high lock wait suggests other issues, but lock conflicts are the most common root cause for write-heavy single-row workloads.
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.
Read latency.
Why it's wrong here
Read latency is separate from write commit latency.
Lock conflicts (e.g., Spanner/API/Lock_wait).
Why this is correct
Lock conflicts directly indicate contention causing commit delays.
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 utilization is capacity-related, not a direct cause of commit latency.
CPU utilization per node.
Why it's wrong here
CPU may be high but does not pinpoint contention.
Common exam traps
Google Cloud often tests the misconception that CPU or storage metrics are the first indicators of write performance issues, when in fact lock contention is the primary driver for high commit latency in transactional workloads.
Detailed technical explanation
Cloud Spanner uses a distributed two-phase commit protocol with pessimistic locking for writes. When multiple transactions attempt to insert into the same row or overlapping index ranges, they queue on row-level locks, increasing commit latency. The lock wait metric captures this queuing time, and high values often indicate a hot spot in the primary key or index design, such as using a monotonically increasing key that causes all inserts to target the same tablet.
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
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 — study guide chapter
Learn the concepts, then practise the questions
Monitor and optimize database performance practice questions
Targeted practice on this topic area only
All PCDE questions
503 questions across all exam domains
Google Professional Cloud Database Engineer study guide
Full concept coverage aligned to exam objectives
PCDE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise PCDE questions linked to Plan and manage database infrastructure.
Practise PCDE questions linked to Define data structures and implement SQL for Business Intelligence.
Practise PCDE questions linked to Design and implement database schemas.
Practise PCDE questions linked to Monitor and optimize database performance.
Practise PCDE questions linked to PCDE fundamentals.
Practise PCDE questions linked to PCDE scenario.
Practise PCDE questions linked to PCDE troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Lock conflicts (e.g., Spanner/API/Lock_wait). — High commit latency for single-row inserts in Cloud Spanner is often caused by lock conflicts, as concurrent transactions may contend for the same row or index. The metric Spanner/API/Lock_wait directly measures time spent waiting for locks, making it the first metric to check. High commit latency without high lock wait suggests other issues, but lock conflicts are the most common root cause for write-heavy single-row workloads.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.