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. 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
2. What is a common bottleneck in GPU performance? → Memory bandwidth
3. 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
4. What is a common technique to prevent overfitting? → Dropout
5. Which architecture component is responsible for executing thousands of threads simultaneously? → Streaming Multiprocessor (SM)
6. Which NVIDIA library is specifically designed to accelerate data loading and augmentation pipelines for deep learning training? → DALI (Data Loading Library)
7. Which technique helps identify race conditions in GPU code? → Using synchronization and detection tools
8. 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
9. Which type of memory has the highest latency in CUDA? → Global memory
10. What is the primary function of the nvidia-smi command-line utility? → Monitoring and managing NVIDIA GPU devices and their status
11. Which of the following best describes a key competency required for edge ai & embedded systems in NCA practice? → Strong analytical skills combined with effective communication and ethical judgment
12. What is the purpose of CUDA memory coalescing? → Group memory accesses for efficiency
13. What is a CUDA kernel? → A GPU function executed in parallel
14. Which term describes the process of breaking a GPU task into smaller pieces executed in parallel? → Parallelism
15. How do GPUs achieve higher throughput than CPUs? → By using thousands of parallel cores
16. What is CUDA-GDB primarily used for in GPU application development? → Interactive debugging of CUDA applications at the thread and warp level
17. In the context of NCA certification, what is the most important consideration when implementing tensor core & mixed precision? → Ensuring alignment with established standards, stakeholder needs, and best practices
18. Which activation function outputs values between 0 and 1? → Sigmoid
19. Which NVIDIA library provides GPU-accelerated implementations of the Basic Linear Algebra Subprograms (BLAS) for dense matrices? → cuBLAS
20. What is the primary function of a GPU? → To process and render graphics efficiently
21. In the context of NCA certification, what is the most important consideration when implementing inference optimization & tensorrt? → Ensuring alignment with established standards, stakeholder needs, and best practices
22. Which of the following best describes a key competency required for containerization & ngc catalog in NCA practice? → Strong analytical skills combined with effective communication and ethical judgment
23. What is overfitting in deep learning? → Model learns noise and poorly generalizes
24. Which of the following best describes a key competency required for tensor core & mixed precision in NCA practice? → Strong analytical skills combined with effective communication and ethical judgment
25. Which debugging tool is designed specifically for CUDA applications? → CUDA-GDB
26. Which NVIDIA Nsight tool provides the most granular hardware performance counter data for optimizing individual GPU kernels? → Nsight Compute
27. What is warp divergence? → Threads take different paths
28. Which of the following best describes a key competency required for inference optimization & tensorrt in NCA practice? → Strong analytical skills combined with effective communication and ethical judgment
29. What is the purpose of a neural network in deep learning? → To model complex data relationships
30. What is backpropagation used for? → Weight updates using gradients