NCA Study Guide 2026
Everything you need to pass the NCA exam in one place: the exam format, every topic to study, real practice questions with explanations, flashcards, and full-length practice tests. Free, no sign-up needed.
📋 NCA Exam Format at a Glance
📚 NCA Topics to Study (63)
✍️ Sample NCA Questions & Answers
1. In data parallelism across multiple GPUs, which operation is performed after each training batch to synchronize model parameters?
AllReduce aggregates gradients from all GPUs and distributes the averaged result so each GPU updates identically.
2. How does TensorRT handle operations not supported by its ONNX parser during model import?
Unsupported ONNX ops cause a parse failure; the developer must register a TensorRT plugin that implements the missing operation on the GPU.
3. What is the most effective way to measure success in nvidia networking & interconnects within NCA professional practice?
Effective measurement combines multiple data sources — quantitative metrics, qualitative assessments, and stakeholder feedback — all aligned with clearly defined objectives for a comprehensive evaluation.
4. Which component of TensorRT is responsible for fusing multiple consecutive layers (e.g., Conv + BatchNorm + ReLU) into a single optimized kernel?
TensorRT's layer fusion optimizer combines compatible adjacent layers into a single kernel, reducing memory bandwidth and kernel launch overhead.
5. Which metric best indicates that Tensor Cores are being heavily utilized in a training workload?
sm__pipe_tensor_cycles_active measures the fraction of cycles where Tensor Core pipelines are active, directly indicating utilization.
6. What does 'occupancy' mean in the context of NVIDIA GPU performance tuning?
Occupancy is the ratio of active warps to the maximum warps an SM can support, and higher occupancy generally enables better latency hiding through warp switching.