Google
PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #81
You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team…
The correct answer is C. Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with. https://cloud.google.com/blog/products/ai-machine-learning/reduce-the-costs-of-ml-workflows- with-preemptible-vms-and-gpus?hl=en
Submitted by yousef_jo· Apr 18, 2026Monitoring, optimizing, and maintaining ML solutions
Question
You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team usually takes a few weeks or months to iterate on a new version of a model. You were recently asked to review your team's spending. How should you reduce your Google Cloud compute costs without impacting the model's performance?
Options
- AUse AI Platform to run distributed training jobs with checkpoints.
- BUse AI Platform to run distributed training jobs without checkpoints.
- CMigrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with
- DMigrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs
How the community answered
(44 responses)- A14% (6)
- B7% (3)
- C59% (26)
- D20% (9)
Explanation
https://cloud.google.com/blog/products/ai-machine-learning/reduce-the-costs-of-ml-workflows- with-preemptible-vms-and-gpus?hl=en
Topics
#Cloud Cost Management#Preemptible Instances#Kubernetes Engine#ML Orchestration
Community Discussion
No community discussion yet for this question.