ASSOCIATE-CLOUD-ENGINEER · Question #396
ASSOCIATE-CLOUD-ENGINEER Question #396: Real Exam Question with Answer & Explanation
The correct answer is D: Use Google Kubernetes Engine (GKE) and hardware accelerators as a platform to run the fine-. GKE with hardware accelerators (e.g., GPUs or TPUs) provides the scalability, flexibility, and control needed to run large-scale ML workloads, such as fine-tuning large language models. It allows efficient resource management, autoscaling, and orchestration of complex ML pipeline
Question
Your company's machine learning team requires a scalable and flexible platform to fine-tune large language models utilizing a large volume of proprietary data on Google Cloud. You are tasked with building a solution for this team. What should you do?
Options
- AUse Dataflow as a platform to run the fine-tuning jobs
- BUse a Compute Engine managed instance group as a platform to deploy Jupyter Notebooks and
- CUse Cloud Run and GPU as a platform to run the fine-tuning jobs.
- DUse Google Kubernetes Engine (GKE) and hardware accelerators as a platform to run the fine-
Explanation
GKE with hardware accelerators (e.g., GPUs or TPUs) provides the scalability, flexibility, and control needed to run large-scale ML workloads, such as fine-tuning large language models. It allows efficient resource management, autoscaling, and orchestration of complex ML pipelines while integrating well with other GCP services.
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