Cloud Digital Leader Scaling with Google Cloud operations Practice Question
A company runs a web application on Compute Engine instances behind a managed instance group with autoscaling based on CPU utilization. After a marketing campaign, traffic spikes and the autoscaler adds instances quickly, but the application becomes slow. What is the most likely cause?
⚠ Common exam trap
Many candidates assume CPU utilization is always the correct metric for scaling, but the question tests the understanding that autoscaling only works well when the chosen metric matches the actual bottleneck of the application.
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
✓
Autoscaler uses CPU utilization but the application is memory-bound
The autoscaler adds instances based on CPU utilization, but if the application is memory-bound, adding more instances does not alleviate memory pressure. Each new instance still runs the same memory-intensive workload, so CPU may remain low while memory is exhausted, causing slowdowns. The autoscaler fails to address the actual bottleneck, leading to poor performance despite scaling out.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Autoscaler uses CPU utilization but the application is memory-bound
Why this is correct
The autoscaler adds instances based on CPU utilization, but if the application is memory-bound, additional instances will still contend for the same memory resources, and each new instance adds per-instance memory overhead. The bottleneck remains, so scaling horizontally on a mismatched metric does not address the root cause; memory stays saturated, and the application continues to experience slowness.
- ✗
Instances are in different zones causing inter-zone latency
Why it's wrong here
Inter-zone latency is typically sub-millisecond to a few milliseconds on Google Cloud's global network, so distributing instances across zones adds negligible delay for most web applications. The primary slowness symptom would not be explained by zone placement unless the communication pattern is extremely chatty and latency-sensitive. Furthermore, a properly configured load balancer routes to the nearest healthy instance, making inter-zone latency an unlikely culprit.
- ✗
Autoscaling cooldown period is too short
Why it's wrong here
A cooldown period that is too short causes the autoscaler to react too frequently, potentially triggering additional scaling operations before newly added instances stabilize. This leads to oscillation and unnecessary instance churn, but it does not make the existing instances slow; the symptom would be autoscaling instability, increased costs, or API throttling. Slowness after scaling points to a performance bottleneck, not timing of scaling decisions.
- ✗
Health check interval is too long
Why it's wrong here
A long health check interval means it takes longer for the load balancer to detect an unhealthy instance and remove it from rotation, so user requests may continue to hit instances that are failing. However, this affects availability and error rates rather than causing a gradual slowdown of healthy instances. The observed slowness after scaling suggests a resource bottleneck or ineffective scaling policy, not the frequency of health probes.
Go deeper
Related to this question
Learn chapter
Compute Options on Google Cloud
Key term
Managed instance group
A managed instance group is a collection of identical virtual machine instances that are automatically managed as a single unit to ensure high availability and scalability.
Key term
Autoscaler
An Autoscaler is a cloud service that automatically increases or decreases the number of virtual machines (instances) or resources based on real-time demand, so your application always has enough capacity without wasting money on idle servers.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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