NCA-AIIO Question 99
Select 3An organization is deploying an AI cluster to support large-scale machine learning workloads. They want to ensure that their cluster orchestrates AI jobs efficiently, balances compute resources, and minimizes idle GPU time. Which of the following are essential considerations for AI cluster orchestration and job scheduling?
- A
Implementing a workload-aware scheduler to optimize GPU utilization
- B
Using a static resource allocation strategy to avoid reconfiguration
- C
Leveraging container orchestration tools like Kubernetes for job management
- D
Enabling job preemption to prioritize high-priority tasks
- E
Disabling GPU monitoring to reduce overhead in the cluster
Show answer and explanation
Correct answers: A, C, D
Explanation
Efficient AI cluster orchestration and job scheduling require dynamic and workload-aware strategies to maximize GPU utilization. Container orchestration tools like Kubernetes provide scalable management for workloads, while features like job preemption ensure prioritization of critical tasks. Static resource allocation and lack of monitoring hinder the overall efficiency and flexibility of the system.
- A. Correct.
Correct: A workload-aware scheduler ensures that jobs are efficiently distributed across GPUs, maximizing resource usage and minimizing idle time.
- B. Incorrect.
Incorrect: Static resource allocation is not ideal for dynamic AI workloads, as it can lead to underutilized resources and reduced flexibility.
- C. Correct.
Correct: Container orchestration tools like Kubernetes are essential for managing AI jobs, scaling workloads, and automating resource allocation.
- D. Correct.
Correct: Job preemption is important for prioritizing critical tasks and maintaining efficient scheduling in AI clusters.
- E. Incorrect.
Incorrect: Disabling GPU monitoring would lead to a lack of visibility into resource usage and potential inefficiencies in the cluster.