NCA Cheat Sheet 2026

The 30 highest-yield NCA facts, distilled from real exam questions. Print it, save it as a PDF, or study it here — free, no sign-up.

1. What is the function of NVIDIA's RT Cores in Turing and later architectures? → Accelerate ray-box and ray-triangle intersection tests for real-time ray tracing
2. In the context of NGC containers, what does 'container image digest' provide compared to a mutable image tag? → An immutable reference to a specific image version ensuring reproducibility
3. What is the recommended approach to converting a PyTorch model for TensorRT deployment? → Export to ONNX first, then parse with TensorRT's ONNX parser
4. What is 'knowledge distillation' in the context of deep learning model compression? → Training a smaller 'student' model to mimic the outputs of a larger 'teacher' model
5. Which metric best indicates that Tensor Cores are being heavily utilized in a training workload? → High sm__pipe_tensor_cycles_active percentage in Nsight Compute
6. Which NVIDIA ConnectX feature accelerates distributed deep learning by offloading collective communication operations to the NIC? → SHARP in-network computing
7. In Nsight Systems, what does a gap between consecutive kernel launches in the GPU timeline most likely indicate? → CPU-side launch overhead or synchronization stall between kernels
8. A company wants to deploy GPU-accelerated Kubernetes workloads. Which NVIDIA component enables GPUs to be scheduled as resources within Kubernetes? → NVIDIA GPU Operator
9. In the context of NCA certification, what is the most important consideration when implementing nvidia networking & interconnects? → Ensuring alignment with established standards, stakeholder needs, and best practices
10. What memory technology do NVIDIA's H100 and A100 GPUs use to achieve high bandwidth in data center workloads? → HBM2e / HBM3
11. Which flag in TensorRT's BuilderConfig enables layer-level profiling to identify per-layer latency bottlenecks? → PROFILING_VERBOSITY set to LAYER_NAMES_ONLY or DETAILED
12. Which memory type on an NVIDIA GPU is shared among all threads within a thread block and has configurable size per SM? → Shared memory
13. Ampere Tensor Cores added support for which sparse computation technique that can double effective throughput? → 2:4 structured sparsity
14. What is a common bottleneck in GPU performance? → Memory bandwidth
15. What is the primary purpose of TensorRT plugins? → To implement custom layers or operations that TensorRT does not natively support
16. Which of the following best describes a key competency required for nvidia networking & interconnects in NCA practice? → Strong analytical skills combined with effective communication and ethical judgment
17. What is a common technique to prevent overfitting? → Dropout
18. Which architecture component is responsible for executing thousands of threads simultaneously? → Streaming Multiprocessor (SM)
19. When serializing a TensorRT engine with engine.serialize(), what format is the output? → A binary blob (plan file) specific to the GPU and TensorRT version used during the build
20. What is the function of the GigaThread Engine in NVIDIA GPU architecture? → Distribute thread blocks across Streaming Multiprocessors
21. Which NVIDIA library provides GPU-accelerated primitives for computer vision preprocessing tasks such as resizing, normalization, and color space conversion? → DALI
22. Which container runtime is officially recommended for deploying AI workloads on Jetson devices to ensure GPU access inside the container? → NVIDIA Container Runtime (nvidia-container-runtime)
23. Which NVIDIA Jetson module is specifically designed for entry-level edge AI applications with the lowest power envelope in the Jetson lineup? → Jetson Nano
24. Which data format is the standard interchange format for exporting trained PyTorch or TensorFlow CV models before TensorRT optimization? → ONNX (.onnx)
25. In InfiniBand networking, what is a 'fat-tree' topology primarily designed to achieve? → Provide non-blocking full bisection bandwidth
26. Which NVIDIA library is specifically designed to accelerate data loading and augmentation pipelines for deep learning training? → DALI (Data Loading Library)
27. Which technique helps identify race conditions in GPU code? → Using synchronization and detection tools
28. Which scaling strategy is most appropriate when a single GPU cannot hold the entire model due to memory constraints? → Model parallelism (pipeline or tensor)
29. In the context of NCA certification, what is the most important consideration when implementing edge ai & embedded systems? → Ensuring alignment with established standards, stakeholder needs, and best practices
30. Which component of TensorRT is responsible for fusing multiple consecutive layers (e.g., Conv + BatchNorm + ReLU) into a single optimized kernel? → Layer fusion optimizer