AAIA AI Operations Practice Question
You are performing an audit on an ML project utilizing Kubeflow. The team is using Katib for hyperparameter tuning. Which configuration step is critical to ensure that individual trials do not starve the production inference service of resources?
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
✓
Implementing Kubernetes ResourceQuotas
Resource quotas in Kubernetes prevent training trials from consuming resources allocated to production services.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increasing node count on the master node
Why it's wrong here
This does not limit resource consumption by individual pods.
- ✗
Disabling auto-scaling on the cluster
Why it's wrong here
Disabling auto-scaling would cause failures rather than managing resources.
- ✗
Setting the Katib algorithm to random search
Why it's wrong here
Search algorithms do not influence resource management.
- ✓
Implementing Kubernetes ResourceQuotas
Why this is correct
ResourceQuotas enforce usage limits on namespaces, preventing trial pods from exhausting cluster resources.
About these practice questions
One of 209 original AAIA practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official ISACA exam blueprint
This AAIA practice question is part of Courseiva's free ISACA 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 AAIA exam.