AI0-001 AI Implementation and Operations Practice Question
A large financial services company deploys multiple AI models on a shared Kubernetes cluster with GPU nodes. The models serve real-time fraud detection and credit scoring. Recently, the operations team observed frequent out-of-memory (OOM) errors during peak hours, causing inference failures. The monitoring dashboards show GPU memory utilization averaging 90% during peak times, and pods are being evicted. The team has allocated 8GB per pod and the total cluster GPU memory is 32GB. The models require at least 4GB each, but the fraud detection model occasionally spikes to 7GB. Which course of action best resolves the OOM errors while maintaining high availability?
⚠ Common exam trap
CompTIA often tests the misconception that simply increasing resource requests or node size solves OOM errors, when the real solution involves proper resource limits and scheduling policies to handle variable workloads and maintain availability.
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
✓
Set resource limits and requests per model based on observed usage, and implement pod priority classes
It uses Kubernetes resource management features—setting precise resource requests and limits based on observed GPU memory usage—combined with pod priority classes to ensure critical fraud detection pods are scheduled and retained during contention. This prevents OOM errors by capping memory per pod while allowing the spike-prone fraud model to be prioritized over less critical workloads, maintaining high availability without overprovisioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the batch size and model complexity for all models to lower memory footprint
Why it's wrong here
May degrade model accuracy and requires re-engineering.
- ✓
Set resource limits and requests per model based on observed usage, and implement pod priority classes
Why this is correct
Limits prevent OOM, priority ensures critical models get resources.
- ✗
Provision larger GPU nodes with 48GB memory each
Why it's wrong here
Vertical scaling is expensive and doesn't enforce limits.
- ✗
Increase the memory request for all pods to 8GB to ensure they have enough
Why it's wrong here
This may exceed cluster capacity and cause scheduling failures.
Visual reference
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.