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Which architecture, training or inference, requires more data storage?
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B
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Explanation
Training architecture generally requires more data storage because training uses large datasets, repeated data access, and checkpointing. NVIDIA DGX SuperPOD storage architecture documentation explains that storage performance requirements vary by model and dataset, and it specifically highlights training workloads, datasets, and checkpoint files as important storage design considerations. For large model use cases, NVIDIA notes that “peak performance for reads and writes are needed for creating and reading checkpoint files” and that training stops during checkpoint operations. NVIDIA also states that storage solutions for AI factories must support “large-scale AI training, fine-tuning, inference, KV cache, and retrieval-augmented generation,” showing that storage is relevant across the lifecycle but especially demanding for training and fine-tuning. Inference architecture typically stores the deployed model, runtime components, and sometimes cache or retrieval data. Training architecture must store training datasets, intermediate outputs, logs, and checkpoints, so it generally has the greater storage requirement. [Reference: NVIDIA DGX SuperPOD Storage Architecture; NVIDIA-Certified Storage documentation.]
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An IT professional is considering whether to implement an on-prem or cloud infrastructure. Which of the following is a key advantage of on-prem infrastructure?
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C
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Explanation
On-premises infrastructure offers a key advantage in ensuring data security and sovereignty, as organizations retain direct control over hardware and data, facilitating compliance with strict regulations (e.g., GDPR). Cloud solutions excel in scalability and lower upfront costs, but on-prem provides unmatched authority over sensitive data, outweighing remote management ease in security-critical scenarios. (Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on On-Prem vs. Cloud Infrastructure) |
What is the maximum number of MIG instances that an H100 GPU provides?
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A
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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. (Reference: NVIDIA H100 GPU Documentation, MIG Section) |
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Find answers to the most common questions about the NVIDIA NCA-AIIO exam, including what it is, how to prepare, and how it can boost your career.
The NVIDIA NCA-AIIO certification is a globally-acknowledged credential that is awarded to candidates who pass this certification exam by obtaining the required passing score. This credential attests and validates the candidates' knowledge and hands-on skills in domains covered in the NVIDIA NCA-AIIO certification syllabus. The NVIDIA NCA-AIIO certified professionals with their verified proficiency and expertise are trusted and welcomed by hiring managers all over the world to perform leading roles in organizations. The success in NVIDIA NCA-AIIO certification exam can be ensured only with a combination of clear knowledge on all exam domains and securing the required practical training. Like any other credential, NVIDIA NCA-AIIO certification may require periodic renewal to stay current with new innovations in the concerned domains.