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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Associate AI Infrastructure and Operations |
| Exam Number: | NCA-AIIO |
| Exam Format: | Multiple Choice, Simulation-Style Questions, Scenario-Based Items |
| Exam Price: | $125 USD |
| Related Certifications: | NVIDIA-Certified Associate |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 50 |
| Available Languages: | English |
| Exam Duration: | 60 minutes |
| Sample Questions: | NVIDIA NCA-AIIO Sample Questions |
| Exam Way: | Online, proctored remotely via Certiverse |
| Pre Condition: | A basic understanding of data center infrastructure |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/ai-infrastructure-operations-associate |
>> NVIDIA NCA-AIIO Test Duration <<
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NEW QUESTION # 15
Your AI data center is experiencing fluctuating workloads where some AI models require significant computational resources at specific times, while others have a steady demand. Which of the following resource management strategies would be most effective in ensuring efficient use of GPU resources across varying workloads?
Answer: B
Explanation:
Implementing NVIDIA MIG (Multi-Instance GPU) for resource partitioning is the most effective strategy for ensuring efficient GPU resource use across fluctuating AI workloads. MIG, available on NVIDIA A100 GPUs, allows a single GPU to be divided into isolated instances with dedicated memory and compute resources. This enables dynamic allocation tailored to workload demands-assigning larger instances to resource-intensive tasks and smaller ones to steady tasks-maximizing utilization and flexibility. NVIDIA's
"MIG User Guide" and "AI Infrastructure and OperationsFundamentals" emphasize MIG's role in optimizing GPU efficiency in data centers with variable workloads.
Round-robin scheduling (A) lacks resource awareness, leading to inefficiency. Manual scheduling (C) is impractical for dynamic workloads. Upgrading GPUs (D) increases capacity but doesn't address allocation efficiency. MIG is NVIDIA's recommended solution for this scenario.
NEW QUESTION # 16
Engineers are troubleshooting slow step-time and poor scaling efficiency in a multi-rack distributed AI training cluster. Which infrastructure change is MOST likely to improve end-to-end training performance?
Answer: A
Explanation:
End-to-end distributed training is often limited by collective communication, especially all-reduce across racks. NVIDIA's NCCL is optimized for high-bandwidth, low-latency interconnects such as InfiniBand and RoCE, and its documentation specifically notes optimization over InfiniBand and RoCE to maximize performance for collectives like all-reduce. A lossless RDMA-capable fabric therefore most directly improves step time and scaling efficiency in multi-node training.
NEW QUESTION # 17
Which NVIDIA tool aids data center monitoring and management?
Answer: B
Explanation:
NVIDIA Data Center GPU Manager (DCGM) aids data center monitoring and management by providing detailed GPU telemetry, health diagnostics, and performance tracking at scale. Clara targets healthcare, TensorRT optimizes inference, and Mellanox Insight isn't a standard NVIDIA tool, making DCGM the go-to solution.
NEW QUESTION # 18
You are responsible for scaling an AI infrastructure that processes real-time data using multiple NVIDIA GPUs. During peak usage, you notice significant delays in data processing times, even though the GPU utilization is below 80%. What is the most likely cause of this bottleneck?
Answer: C
Explanation:
Inefficient data transfer between nodes in the cluster (D) is the most likely cause of delays when GPU utilization is below 80%. In a multi-GPU setup processing real-time data, bottlenecks often arise from slow inter-node communication rather than GPU compute capacity. If data cannot move quickly between nodes (e.
g., due to suboptimal networking like low-bandwidth Ethernet instead of InfiniBand or NVLink), GPUs wait idle, causing delays despite low utilization.
* High CPU usage(A) could bottleneck preprocessing, but GPU utilization would likely be even lower if CPUs were the sole issue.
* Overprovisioning(B) would result in idle GPUs, but not necessarily delays unless misconfigured.
* Insufficient memory bandwidth(C) would typically push GPU utilization higher, not keep it below
80%.
NVIDIA recommends high-speed interconnects (e.g., NVLink, InfiniBand) for efficient data transfer in distributed AI setups (D).
NEW QUESTION # 19
What is the maximum number of MIG instances that an H100 GPU provides?
Answer: C
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)
NEW QUESTION # 20
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