NCA Foundations case studies
NCA Foundations · Put your knowledge to work in context.
NCA Foundations is an entry-level certification track from NVIDIA for people starting careers around accelerated computing and AI. It suits students, career changers, IT and data professionals, and developers who want a documented baseline in how NVIDIA hardware and software fit together before specializing in deep learning, data science, or infrastructure operations. The exam spans eight domains: platform and architecture fundamentals, GPU architecture and parallel computing concepts, the software stack and CUDA ecosystem, AI and deep learning workflow fundamentals, accelerated data science, deployment and inference basics, systems operations and troubleshooting, and NVIDIA solutions and professional practices. The AI workflow domain carries the largest share, so expect questions about training versus inference, data pipelines, and model lifecycle concepts rather than obscure API details. The NCA Foundations case studies are 5 cases and about 3,069 words.
1Sizing a Multi-Node Training Cluster for a Fixed Budget
A research lab has 8 DGX-class nodes, each with 8 GPUs and 640 GB of GPU memory total per node, connected by a non-blocking InfiniBand fabric. They want to train a 70B-parameter dense transformer on 4 trillion tokens within a 6-week window.
699 words, about 3 min2Diagnosing Low GPU Utilization in an Inference Service
A team runs a real-time image classification service on 4 GPUs. Each GPU hosts one model instance.
622 words, about 3 min3Choosing Between Custom CUDA, Libraries, and Framework Operators
A medical imaging startup has a preprocessing step that resamples 3D volumes. The current implementation uses a Python loop calling a deep learning framework's interpolation operator once per slice.
614 words, about 3 min4Deciding Whether a Model Is Ready for Production
A team has fine-tuned a language model for customer support. On their held-out test set, accuracy is 91%, up from 84% for the previous model.
591 words, about 3 min5Troubleshooting Intermittent GPU Failures in a Shared Cluster
A shared research cluster has 32 GPU nodes. Users report that long training jobs fail intermittently, roughly once every 20 hours per job.
543 words, about 3 min