Google Cloud Associate Cloud Engineer — Question 283

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?

Answer options

• A. Use Dataflow as a platform to run the fine-tuning jobs
• B. Use a Compute Engine managed instance group as a platform to deploy Jupyter Notebooks and run fine-tuning jobs.
• C. Use Cloud Run and GPU as a platform to run the fine-tuning jobs.
• D. Use Google Kubernetes Engine (GKE) and hardware accelerators as a platform to run the fine-tuning jobs.

Correct answer: D

Explanation

The correct answer is D because Google Kubernetes Engine (GKE) provides a robust and scalable environment that can effectively manage the deployment of machine learning workloads, especially when using hardware accelerators like GPUs. Options A and C do not offer the same level of flexibility and scalability needed for large language models, while option B is more suited for single-instance deployments rather than the distributed computing required for large-scale fine-tuning.