Cloud Digital Leader Why cloud technology is transforming business Practice Question
A retail company experiences huge traffic spikes during Black Friday and slow periods otherwise. They want to avoid over-provisioning servers and reduce costs. Which cloud feature directly addresses this need?
⚠ Common exam trap
Google Cloud often tests the misconception that load balancing alone solves capacity issues, but candidates must recognize that load balancing distributes existing traffic and does not add or remove servers—only auto-scaling handles dynamic provisioning.
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
✓
Auto-scaling based on CPU utilization
Auto-scaling based on CPU utilization dynamically adjusts the number of server instances in response to real-time demand. During Black Friday traffic spikes, it automatically adds capacity, and during slow periods, it scales down to reduce costs. This directly addresses the need to avoid over-provisioning while maintaining performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Auto-scaling based on CPU utilization
Why this is correct
Auto-scaling based on CPU utilization is the correct choice because it dynamically adjusts the number of compute instances in response to real-time CPU load. When traffic spikes during Black Friday, the autoscaler provisions additional VM instances to keep CPU utilization within a target threshold; when demand drops, it removes excess instances to prevent overprovisioning. This elasticity directly matches compute resources to demand, minimizing idle capacity and cost. It avoids both under-provisioning (downtime) and over-provisioning (waste), making it ideal for unpredictable traffic surges.
- ✗
Purchasing committed use discounts
Why it's wrong here
Purchasing committed use discounts is a cost-saving mechanism that requires a 1-year or 3-year commitment to a consistent baseline of compute resources. While it provides a significant discount, it locks you into paying for a fixed capacity regardless of actual usage. During a Black Friday spike, you would either pay for extra on-demand resources (losing the discount on that burst) or, if you commit to high capacity, you pay for idle CPUs during normal off-peak periods. This model conflicts with highly variable demand, as it assumes steady-state workloads, not unpredictable surges.
- ✗
Load balancing across regions
Why it's wrong here
Load balancing across regions distributes incoming traffic across multiple backend instances or regional clusters, improving latency and fault tolerance. However, a load balancer does not itself add or remove compute capacity; it only routes requests to existing, healthy backends. If all backend instances are saturated due to a Black Friday spike, adding more regions does not create new resources—you still need to scale up the underlying instance groups. Load balancing complements autoscaling but cannot alone handle traffic spikes, so it is not a substitute for dynamic capacity adjustment.
- ✗
Manual scaling with reserve instances
Why it's wrong here
Manual scaling with reserve instances combines human-driven capacity decisions with long-term resource commitments, which is poorly suited for spiky traffic. An operator would need to anticipate the spike in advance and manually modify the managed instance group size, which is slow, error-prone, and cannot react to sudden demand changes—especially if the spike exceeds forecasts. Reserved instances further exacerbate this by locking you into a fixed capacity that you pay for 24/7, meaning you either overpay for idle resources during low demand or run short of capacity during the spike. This approach lacks the automation and elasticity needed for rapid, unpredictable traffic surges.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This GCDL 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 GCDL exam.