The answer is to increase the maximum number of instances in the MIG and enable autoscaling based on HTTP load balancing utilization. When a managed instance group hits its max instances with CPU at 90%, the bottleneck is capacity, not just scaling logic; raising the max ceiling immediately allows new instances to spin up and absorb the traffic causing the HTTP 503 errors. Pairing this with HTTP load balancing utilization as the autoscaling metric is critical because it measures the actual request queue depth at the load balancer, which is far more responsive to sudden spikes than CPU utilization alone—CPU often lags behind real demand. On the Google Professional Cloud DevOps Engineer exam, this scenario tests your understanding that autoscaling metrics must match the symptom: 503s indicate the backend is overwhelmed, so you need both a higher capacity limit and a metric that directly reflects frontend pressure. A common trap is to only adjust CPU thresholds, but that ignores the root cause of hitting the instance cap. Memory tip: “503 means no room—raise the roof and watch the load balancer’s queue.”
PCDOE Managing service incidents Practice Question
This PCDOE practice question tests your understanding of managing service incidents. 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.
A service experiences increased latency and HTTP 503 errors. The engineer finds that the backend managed instance group (MIG) is at max instances and CPU utilization is 90%. Which TWO actions should the engineer take to restore the service quickly?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable autoscaling based on HTTP load balancing utilization
Option A is correct because enabling autoscaling based on HTTP load balancing utilization allows the MIG to scale out based on the actual request load, which directly addresses the 503 errors caused by the backend being at max capacity. This metric is more responsive to traffic spikes than CPU utilization alone, as it reflects the frontend load balancer's view of backend capacity.
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 autoscaling based on HTTP load balancing utilization
Why this is correct
Scales based on request rate, which is more responsive than CPU.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the autoscaling target CPU utilization to 95%
Why it's wrong here
Higher target would delay scaling, worsening the issue.
✓
Increase the maximum number of instances in the MIG
Why this is correct
Allows the MIG to scale out further to handle load.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce the autoscaling target CPU utilization to 50%
Why it's wrong here
Lower target would trigger more instances but the MIG is already at max; need to increase max instances first.
✗
Reduce the number of instances to avoid resource contention
Why it's wrong here
Reducing instances would increase load on remaining instances.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that adjusting CPU utilization thresholds (either up or down) is a quick fix for capacity issues, when in fact the immediate solution is to increase the maximum instance count or enable a more responsive scaling metric.
Detailed technical explanation
How to think about this question
Autoscaling based on HTTP load balancing utilization uses the 'HTTP(S) Load Balancing utilization' metric, which is calculated as the ratio of requests per second to the target capacity of the backend. This metric is more granular than CPU utilization because it directly reflects the load balancer's health checks and request queue depth. In a real-world scenario, a sudden traffic spike from a marketing campaign could cause 503 errors even if CPU is only at 70%, because the load balancer's connection pool is exhausted—HTTP utilization-based scaling would detect this and scale out faster.
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.
Managing service incidents — This question tests Managing service incidents — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable autoscaling based on HTTP load balancing utilization — Option A is correct because enabling autoscaling based on HTTP load balancing utilization allows the MIG to scale out based on the actual request load, which directly addresses the 503 errors caused by the backend being at max capacity. This metric is more responsive to traffic spikes than CPU utilization alone, as it reflects the frontend load balancer's view of backend capacity.
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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