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 buil

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

Submitted by ngozi_ng· Mar 30, 2026Planning and configuring a cloud solution

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-

How the community answered

(43 responses)

A
5% (2)
B
7% (3)
C
12% (5)
D
77% (33)

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.

Topics

#GKE#hardware accelerators#GPU#LLM fine-tuning

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