Google ACE Practice Question: Ensuring Successful Operation of a Cloud Solution
You are deploying a GKE cluster with node autoscaling enabled. The cluster runs batch jobs that are sensitive to startup latency. You notice that during scale-up, new nodes take several minutes to become ready. Which action can reduce the time it takes for new nodes to join the cluster?
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
✓
Use a custom image with pre-installed dependencies
Using a custom image with pre-installed dependencies reduces the time needed for node initialization because the image already contains the required software, avoiding downloads during startup. This is especially beneficial for batch jobs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the initial node pool size
Why it's wrong here
Increasing the initial node pool size only defines the cluster's starting capacity; it does not affect the time the cluster autoscaler needs to provision additional nodes when demand rises. The initial size simply creates that many nodes up front, so while you may have more capacity immediately, the latency of any subsequent scale-out—instance boot, image load, and kubelet startup—remains unchanged. In fact, over-provisioning the initial size wastes cost and still doesn't reduce node startup time for new nodes.
- ✗
Set the --max-nodes-per-pool flag to a higher value
Why it's wrong here
The --max-nodes-per-pool flag configures the maximum number of nodes that a managed instance group can scale to; it is an upper bound for the cluster autoscaler, not a tuning knob for node provisioning speed. Changing this value only allows the autoscaler to create more nodes, but each node still goes through the same boot and configuration sequence. Node startup speed is governed by factors like the machine image, pod density, and startup scripts, none of which are influenced by this limit.
- ✓
Use a custom image with pre-installed dependencies
Why this is correct
Using a custom image with pre-installed dependencies is the correct approach because it directly reduces node initialization time. A custom image can bake in the container runtime, required OS packages, and even pre-cached application container images, avoiding the typical runtime download and configuration steps when a new node is added. When the cluster autoscaler triggers a scale-out, these nodes become schedulable faster, so pending pods are scheduled more quickly.
- ✗
Enable cluster autoscaler with --enable-autorepair
Why it's wrong here
Enabling the cluster autoscaler with --enable-autorepair does not speed up node creation; node auto-repair is a health-management feature that detects and recreates nodes that fail health checks. Since node autoscaling is already on in this scenario, this flag has nothing to do with reducing time to schedule new nodes during scale-out. In fact, if a node becomes unhealthy during a scale-up event, autorepair might trigger a replacement, which does not shorten initial provisioning latency.
Go deeper
Related to this question
Learn chapter
GCP IAM Roles: Primitive, Predefined, Custom
Key term
GKE
GKE is Google's managed Kubernetes service that automates deploying, scaling, and managing containerized applications in the cloud.
Key term
Image
An image is a complete snapshot of a system's operating system, applications, and settings, used to deploy or restore computing environments quickly.
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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.