100% Pass Quiz 2026 Pass-Sure NCP-AIO: NVIDIA AI Operations Exam Paper Pdf

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NVIDIA NCP-AIO Exam Syllabus Topics:

Section	Weight	Objectives
Installation and Deployment	32%	- AI cluster setup and configuration - Driver and firmware installation - Container orchestration and resource management - NVIDIA software stack deployment
Workload Management	20%	- Data pipeline and storage integration - AI workload deployment and scaling - Job scheduling and queue management - Framework and runtime configuration
Troubleshooting and Optimization	20%	- System health and reliability maintenance - Performance monitoring and analysis - Fault diagnosis and resolution - Throughput and latency optimization
Management	28%	- GPU cluster and compute node management - Access control and security policies - Software and container lifecycle management - Resource allocation and scheduling

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NVIDIA AI Operations Sample Questions (Q15-Q20):

NEW QUESTION # 15
You are using NVSHMEM for a large-scale simulation. The application is crashing with segmentation faults. After checking the code for memory errors, you suspect an issue with NVSHMEM configuration. Which of the following environment variables is MOST likely to be misconfigured and causing the crashes?

A. NCCL DEBUG
B. CUDA VISIBLE DEVICES
C. NVSHMEM SYMMETRIC SIZE
D. LD LIBRARY PATH
E. CUDA DEVICE ORDER

Answer: C

Explanation:
NVSHMEM SYMMETRIC SIZE defines the size of the symmetric heap, which is the shared memory region accessible by all processes. If this value is too small, it can lead to segmentation faults when the application tries to allocate more memory than available. The other variables are less directly related to memory allocation within the NVSHMEM environment. While CUDA VISIBLE DEVICES affects GPU visibility, it won't cause segmentation faults related to symmetric memory allocation. LD_LIBRARY_PATH is for finding libraries, not memory. NCCL_DEBUG controls debugging output. CUDA DEVICE_ORDER affects device enumeration.

NEW QUESTION # 16
You have multiple users sharing a server with a single NVIDIAA100 GPU. Two users, Alice and Bob, want to run deep learning experiments concurrently. Alice's job requires 20GB of GPU memory and 30% of compute, while Bob's job needs IOGB of GPU memory and 20% of compute. How can you use MIG to optimally configure the GPU to accommodate both users' requirements?

A. Create two MIG instances: one 3g.20gb instance for Alice and one lg.5gb instance for Bob.
B. Do not use MIG; let both users share the entire GPU.
C. Create two MIG instances: one lg.5gb instance for Alice and one lg.5gb instance for Bob.
D. Create two MIG instances: one 4g.20gb instance for Alice and one 2g.10gb instance for Bob.
E. Create one MIG instance for Alice and let Bob use the remaining GPU resources.

Answer: D

Explanation:
This question challenges understanding of MIG instance sizes. Options A and B are not correct because they allocate insufficient memory to Alice. Option C is not correct because it does not provide dedicated resources for Bob. Option E means that Alice's job is resource intensive. The correct answer is D because it ensures that both Alice and Bob get at least the memory they need and some compute resource allocation. 4g.20gb and 2g.10gb instances ensure allocation of resources required for both users independently.

NEW QUESTION # 17
Consider this YAML snippet for deploying the NVIDIA device plugin. Which statement is true about the highlighted segment?

A. It will make sure that only tolerations are set, but there's no affinity.
B. It's a deprecated way of defining affinities. Consider using nodeAffinity instead.
C. It will make sure that the node affinity is ignored during scheduling.
D. It will make sure that the device plugin gets deployed on nodes with 'accelerator: nvidia-tesla-t4' label
E. This will ensure that the pod is only deployed on the node with certain taints, but with no other scheduling requirements

Answer: D

Explanation:
The 'nodeselector' is used to target the deployment to nodes with label 'accelerator: nvidia-tesla-t4'. "nodeAffinity' is a more advanced way of doing this and is recommended, but in the absence of explicit nodeAffinity, nodeSelector is sufficient.

NEW QUESTION # 18
You're deploying a DOCA-based firewall application on a BlueField-2 DPU. The application uses eBPF for packet filtering. What is the primary reason for using eBPF in this scenario?

A. To automatically generate iptables rules on the host server.
B. To reduce CPU utilization on the host server by offloading packet filtering to the DPU.
C. To improve the compatibility with legacy network devices.
D. To simplify the firewall rule definition using a higher-level language.
E. To enable dynamic updates to the firewall rules without requiring kernel module recompilation.

Answer: B,E

Explanation:
eBPF allows offloading packet filtering to the DPU, thus reducing the load on the host CPU. It also allows dynamic updates to firewall rules without requiring kernel recompilation, which is a significant advantage in terms of flexibility and maintenance.

NEW QUESTION # 19
Which metric is most appropriate for evaluating classification models when dealing with highly imbalanced datasets where one class significantly outnumbers the other?

A. Mean squared error
B. R-squared
C. Accuracy
D. Precision-recall F1 score

Answer: D

Explanation:
Accuracy can be misleading in imbalanced datasets. Precision, recall, and F1 score provide better insight into model performance, especially for minority classes. They help ensure the model correctly identifies important but rare events.

NEW QUESTION # 20
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