mediumMultiple ChoiceObjective-mapped
Google ACE Practice Question: A web application uses a managed instance group
A web application uses a managed instance group. Traffic spikes sharply between 9 AM and 5 PM and drops to near zero overnight. Which autoscaling metric most directly triggers scale-out before user experience degrades?
⚠ Common exam trap
Candidates often assume CPU utilization is the universal autoscaling metric, but the ACE exam specifically tests that for web applications with sharp traffic spikes, the HTTP load balancing serving capacity metric provides the fastest and most direct signal to prevent user experience degradation.
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
✓
HTTP load balancing serving capacity (requests per second per instance)
HTTP load balancing serving capacity (requests per second per instance) is the most direct metric because it measures the actual user-facing load on each instance. When traffic spikes, this metric rises immediately, triggering scale-out before instances become saturated and response times degrade. CPU utilization can lag behind the spike due to queuing or async processing, making it less responsive for sharp traffic patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Average CPU utilization of instances in the group
Why it's wrong here
CPU utilization is a reactive, infrastructure-level metric: it only rises after incoming requests have already consumed processing cycles. Autoscaling policies based on CPU typically require sustained high usage to trigger, so they cannot anticipate sudden traffic bursts and often scale out only after user-visible latency has increased. Additionally, background jobs or periodic maintenance tasks can inflate CPU usage, making it a noisy signal that doesn't cleanly represent actual HTTP request load.
- ✗
Pub/Sub subscription queue depth
Why it's wrong here
Pub/Sub subscription queue depth is designed for asynchronous, message-driven workers that pull and process queued tasks. A typical HTTP request-driven web application behind a load balancer is synchronous—requests arrive directly and must be served immediately, without an intermediate queued backlog. Unless the application explicitly decouples request handling via a thread pool bounded by a subscription, queue depth will stay near zero under load and provides no meaningful correlation with inbound HTTP traffic.
- ✓
HTTP load balancing serving capacity (requests per second per instance)
Why this is correct
HTTP load balancing serving capacity, measured as requests per second per instance, directly reflects the real ingress rate seen by the load balancer. This metric is proactive because it reacts to request count in real time, allowing the managed instance group to scale out before CPU or memory saturation causes latency spikes. Since it is measured at the load balancer, it captures all client traffic destined for the web tier, making it the most accurate and responsive signal for autoscaling stateless HTTP web workloads.
- ✗
Disk I/O throughput
Why it's wrong here
Disk I/O throughput measures the rate of read/write operations to persistent disks, which is a critical signal for storage-bound workloads like databases or data processing pipelines. For a typical stateless web serving tier, disk access is usually cached, small, or not on the critical path, so it does not correlate with HTTP request volume or end-user response times. Scaling on disk I/O could trigger scale-out during a database backup or batch job while ignoring sudden web traffic spikes, leading to inefficient and inaccurate capacity decisions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This ACE 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 ACE exam.