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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #81

PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #81: Real Exam Question with Answer & Explanation

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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

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Topics

#Cloud Cost Management#Preemptible Instances#Kubernetes Engine#ML Orchestration
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