The correct answer is C because fine-tuning large language models requires scalable compute orchestration and hardware acceleration. GKE is appropriate for containerized ML workloads that need flexible scheduling, repeatability, and access to accelerator-backed nodes. Google Cloud documentation specifically supports running compute-intensive workloads such as AI workloads on GKE Standard node pools with NVIDIA GPU hardware accelerators. Dataflow is primarily for data processing pipelines, not model fine-tuning. A managed instance group with Jupyter Notebooks is less flexible and less Kubernetes-native for distributed ML execution. Cloud Run can be useful for serverless services and some GPU workloads, but large-scale training and fine-tuning are better matched to GKE with accelerators.