The answer is to analyze Cloud Monitoring metrics for the load balancer, including backend latency and request counts. This is correct because when diagnosing high latency on a TCP/UDP Network Load Balancer, the most targeted approach is to isolate where the delay occurs—Cloud Monitoring provides granular metrics like backend latency (time spent waiting on instances) and request counts, which directly reveal if the bottleneck is at the load balancer layer or the backend, especially since instance CPU is below 50% and logs show no errors. On the Google Professional Cloud DevOps Engineer exam, this tests your ability to leverage observability tools before making architectural changes; a common trap is jumping to instance-level debugging or altering load balancer settings without first checking these metrics. Remember the mnemonic "BLRC" for Backend Latency, Request Counts—these two metrics pinpoint whether the issue is network or compute.
PCDOE Optimizing service performance Practice Question
This PCDOE practice question tests your understanding of optimizing service 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.
A company runs a critical application on Compute Engine instances behind a TCP/UDP Network Load Balancer. They notice intermittent high latency for a subset of users. The application logs show no errors, and instance CPU is below 50%. Which next step is most effective to diagnose the latency?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Analyze Cloud Monitoring metrics for the load balancer, including backend latency and request counts.
Option D is correct because Cloud Monitoring provides detailed metrics for TCP/UDP Network Load Balancers, including backend latency and request counts, which directly help identify whether the latency originates from the load balancer itself or the backend instances. Since instance CPU is below 50% and application logs show no errors, the issue is likely at the network or load balancer level, and these metrics offer the most targeted diagnostic data without changing the architecture.
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.
✗
Increase the number of instances behind the load balancer.
Why it's wrong here
CPU is low; more instances may not reduce latency.
✗
Enable VPC Flow Logs and analyze for dropped packets.
Why it's wrong here
Flow logs show traffic but not latency.
✗
Switch to an HTTP(S) Load Balancer for better visibility.
Why it's wrong here
Changing load balancer type is disruptive and may not solve the issue.
✓
Analyze Cloud Monitoring metrics for the load balancer, including backend latency and request counts.
Why this is correct
These metrics pinpoint where latency occurs.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume VPC Flow Logs (Option B) are the go-to tool for diagnosing latency, but they only show flow-level metadata and not latency metrics, whereas Cloud Monitoring provides the specific performance data needed for this scenario.
Trap categories for this question
Command / output trap
Flow logs show traffic but not latency.
Detailed technical explanation
How to think about this question
Cloud Monitoring for Network Load Balancers exposes metrics like 'backend_latencies' (p50, p95, p99) and 'request_bytes_count', which are collected from the load balancer's forwarding rules and backend services. These metrics can reveal if latency spikes correlate with specific backends or time periods, helping isolate issues such as asymmetric routing, backend health check failures, or regional network congestion. In a real-world scenario, a sudden increase in p99 latency with stable CPU often points to a backend instance experiencing slow I/O or a network bottleneck at the load balancer's internal proxy.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: Analyze Cloud Monitoring metrics for the load balancer, including backend latency and request counts. — Option D is correct because Cloud Monitoring provides detailed metrics for TCP/UDP Network Load Balancers, including backend latency and request counts, which directly help identify whether the latency originates from the load balancer itself or the backend instances. Since instance CPU is below 50% and application logs show no errors, the issue is likely at the network or load balancer level, and these metrics offer the most targeted diagnostic data without changing the architecture.
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.
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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