- A
Create a backend service with the backend MIG and attach it to the internal TCP/UDP load balancer, enabling connection draining.
Why wrong: Connection draining helps existing connections complete but does not enable autoscaling based on load.
- B
Configure the internal TCP/UDP load balancer with a health check that monitors CPU utilization and adjust the autoscaling metric of the backend MIG accordingly.
Why wrong: Internal TCP/UDP load balancers do not support custom health checks based on CPU; they only support health checks based on response to probes.
- C
Replace the internal TCP/UDP load balancer with an internal HTTP(S) load balancer and configure the backend MIG to autoscale based on the load balancing serving capacity or request count.
Internal HTTP(S) load balancer supports autoscaling based on request rate, allowing the backend to scale with traffic.
- D
Enable Cloud Armor on the external load balancer to rate-limit requests and prevent backend overload.
Why wrong: Cloud Armor protects against attacks but does not address the backend scaling issue.
Quick Answer
The correct answer is to replace the internal TCP/UDP load balancer with an internal HTTP(S) load balancer and configure the backend MIG to autoscale based on load balancing serving capacity or request count. This works because a layer 4 TCP/UDP load balancer cannot expose request-level metrics like requests per second, which are essential for proactive autoscaling of a synchronous REST API backend; instead, it only forwards packets, forcing the MIG to rely on lagging CPU utilization. The internal HTTP(S) load balancer, operating at layer 7, provides real-time serving capacity and request count metrics that directly reflect frontend demand, enabling the backend to scale ahead of CPU spikes. On the Google Professional Cloud Network Engineer exam, this scenario tests your understanding of the critical difference between layer 4 and layer 7 load balancing for autoscaling—a common trap is assuming CPU-based autoscaling is sufficient for synchronous APIs. Memory tip: “Layer 4 for packets, Layer 7 for requests—if your app talks REST, use the one that knows the request.”
PCNE Configuring network services Practice Question
This PCNE practice question tests your understanding of configuring network services. 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 multi-tier web application on Google Cloud. The frontend is in us-central1 (3 instances behind an external HTTP(S) Load Balancer), the backend is in us-west1 (3 instances behind an internal TCP/UDP Load Balancer). The frontend instances are in a managed instance group (MIG) with autoscaling based on CPU utilization. Recently, you noticed that during traffic spikes, the frontend instances' CPU utilization remains low, but the backend instances' CPU utilization spikes to 90% and causes timeouts. The application uses a synchronous REST API; the frontend instances make requests to the internal load balancer's IP. What should you do to resolve the backend scaling issue?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Replace the internal TCP/UDP load balancer with an internal HTTP(S) load balancer and configure the backend MIG to autoscale based on the load balancing serving capacity or request count.
Option C is correct because the internal TCP/UDP load balancer cannot provide request-level metrics (like requests per second) for autoscaling, as it operates at layer 4. Replacing it with an internal HTTP(S) load balancer (layer 7) allows the backend MIG to autoscale based on the load balancing serving capacity or request count, which directly correlates with the frontend's synchronous REST API calls. This resolves the backend CPU spike issue by scaling the backend instances before they become overloaded, rather than relying on CPU utilization which lags behind traffic spikes.
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 a backend service with the backend MIG and attach it to the internal TCP/UDP load balancer, enabling connection draining.
Why it's wrong here
Connection draining helps existing connections complete but does not enable autoscaling based on load.
- ✗
Configure the internal TCP/UDP load balancer with a health check that monitors CPU utilization and adjust the autoscaling metric of the backend MIG accordingly.
- ✓
Replace the internal TCP/UDP load balancer with an internal HTTP(S) load balancer and configure the backend MIG to autoscale based on the load balancing serving capacity or request count.
Why this is correct
Internal HTTP(S) load balancer supports autoscaling based on request rate, allowing the backend to scale with traffic.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable Cloud Armor on the external load balancer to rate-limit requests and prevent backend overload.
Why it's wrong here
Cloud Armor protects against attacks but does not address the backend scaling issue.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume CPU-based autoscaling is sufficient for all tiers, but Cisco tests the nuance that synchronous REST APIs require layer-7 load balancing to expose request-level metrics for proactive autoscaling, while layer-4 load balancers only provide connection-level metrics that lag behind traffic spikes.
Detailed technical explanation
How to think about this question
An internal HTTP(S) load balancer uses Envoy-based proxies that provide request-level metrics (e.g., requests per second, latency) to the backend MIG's autoscaler via the 'serving capacity' metric. This is more responsive than CPU-based autoscaling because it directly reflects incoming load from the frontend's synchronous REST API calls. In contrast, the internal TCP/UDP load balancer only distributes traffic at layer 4, offering no application-layer metrics, so the backend MIG must rely on CPU utilization, which spikes only after requests are already queued and processing, leading to timeouts.
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.
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FAQ
Questions learners often ask
What does this PCNE question test?
Configuring network services — This question tests Configuring network services — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Replace the internal TCP/UDP load balancer with an internal HTTP(S) load balancer and configure the backend MIG to autoscale based on the load balancing serving capacity or request count. — Option C is correct because the internal TCP/UDP load balancer cannot provide request-level metrics (like requests per second) for autoscaling, as it operates at layer 4. Replacing it with an internal HTTP(S) load balancer (layer 7) allows the backend MIG to autoscale based on the load balancing serving capacity or request count, which directly correlates with the frontend's synchronous REST API calls. This resolves the backend CPU spike issue by scaling the backend instances before they become overloaded, rather than relying on CPU utilization which lags behind traffic spikes.
What should I do if I get this PCNE 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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Last reviewed: Jun 11, 2026
This PCNE 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 PCNE exam.
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