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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) |
| Exam Number: | NCA-AIIO |
| Certificate Validity Period: | 2 years |
| Available Languages: | English |
| Related Certifications: | NVIDIA-Certified Professional (various tracks) NVIDIA-Certified Associate: Generative AI LLMs |
| Exam Format: | Multiple choice, Multiple select |
| Exam Duration: | 90 minutes |
| Recommended Training: | NVIDIA Deep Learning Institute (DLI) |
| Exam Registration: | NVIDIA Certification Portal |
| Sample Questions: | NVIDIA NCA-AIIO Sample Questions |
| Exam Way: | Online proctored exam (remote), typically delivered via authorized certification platform |
| Pre Condition: | No formal prerequisites required; recommended familiarity with basic AI/ML concepts and IT infrastructure. |
| Official Syllabus URL: | https://www.nvidia.com/en-us/training/certification/ |
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NEW QUESTION # 51
The data center administrator is asked to deploy infrastructure to support training of a large natural language processing model with a billion parameters and they need the fastest method to train the model. What should the administrator recommend to support this?
Answer: C
Explanation:
NVIDIA DGX SuperPOD is a large-scale, multi-node GPU cluster optimized for training massive AI models with billions of parameters, providing the fastest training times through massive parallelism and high-speed interconnects.
NEW QUESTION # 52
Which aspect of computing uses large amounts of data to train complex neural networks?
Answer: B
Explanation:
Deep learning, a subset of machine learning, relies on large datasets to train multi-layered neural networks, enabling them to learn hierarchical feature representations and complex patterns autonomously. While machine learning encompasses broader techniques (some requiring less data), deep learning's dependence on vast data volumes distinguishes it. Inferencing, the application of trained models, typically uses smaller, real-time inputs rather than extensive training data.
NEW QUESTION # 53
Which aspect of computing uses large amounts of data to train complex neural networks?
Answer: B
Explanation:
Deep learning, a subset of machine learning, relies on large datasets to train multi-layered neural networks, enabling them to learn hierarchical feature representations and complex patterns autonomously. While machine learning encompasses broader techniques (some requiring less data), deep learning's dependence on vast data volumes distinguishes it. Inferencing, the application of trained models, typically uses smaller, real- time inputs rather than extensive training data.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Deep Learning Fundamentals)
NEW QUESTION # 54
What is the maximum number of MIG instances that an H100 GPU provides?
Answer: A
Explanation:
The NVIDIA H100 GPU supports up to 7 Multi-Instance GPU (MIG) partitions, allowing it to be divided into seven isolated instances for multi-tenant or mixed workloads. This capability leverages the H100's architecture to maximize resource flexibility and efficiency, with 7 being the documented maximum.
NEW QUESTION # 55
How many distinct network fabrics are in an AI cluster?
Answer: A
Explanation:
An AI cluster typically employs three distinct network fabrics: one for management and client traffic (e.g., Ethernet), one for storage I/O (e.g., accessing datasets), and one for low-latency RDMA interconnects (e.g., InfiniBand or RoCE) between compute nodes for tasks like gradient synchronization. This separation optimizes performance, scalability, and reliability, distinguishing AI clusters from simpler setups.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Network Fabrics in AI Clusters)
NEW QUESTION # 56
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