NVIDIA NCP-AIO: NVIDIA-Certified Professional: AI Operations

Last checked October 22, 2026 against the official NVIDIA exam page.

1. Question 1Infrastructure and cluster management

   An AI cluster has GPU nodes with different driver versions, causing jobs to fail on some nodes. Which practice keeps the GPU software stack consistent?

   1. AManage driver and container toolkit versions through a common node configuration or GPU operator
   2. BDisable GPU resources on each node and let the workloads fall back to CPU-only execution for consistency
   3. CLet each user install their own driver and CUDA runtime inside a personal environment on the node for speed
   4. DInstall drivers by hand on each node at irregular times whenever an engineer has free time between other tickets
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   Correct answer: A

   A managed configuration or GPU operator keeps driver and runtime versions consistent across nodes. Manual, user-level, or disabled GPU approaches reintroduce inconsistency or remove capacity.

2. Question 2Monitoring and observability

   An operations team wants alerts when GPU utilization stays near zero for a long period while jobs are queued. What does this pattern most likely indicate?

   1. AA network cable that is too long between the switch and the server, causing jitter
   2. BA scheduling or data-loading bottleneck, with GPUs idle while work waits
   3. CA printer problem in the office queue that stalls the shared print spooler
   4. DA healthy, fully utilized cluster where every GPU is busy
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   Correct answer: B

   Idle GPUs with queued jobs indicate scheduling or input bottlenecks such as slow data loading. A fully utilized cluster would not show idle GPUs while work waits, and printers and cable lengths are unrelated.

3. Question 3Workload scheduling

   A research group needs fair sharing of GPUs among several teams, with batch jobs queued and priorities respected. Which scheduler approach fits best?

   1. ARunning queued jobs concurrently on one shared GPU without memory partitioning
   2. BFirst-come, first-served queue with no time, quota, or priority limits in place
   3. CManual allocation of GPUs through email requests reviewed by a single admin who assigns devices by hand
   4. DA workload scheduler such as Slurm or a Kubernetes-based GPU scheduler with queues and quotas
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   Correct answer: D

   Schedulers with queues, quotas, and priorities provide fair sharing and efficient use. Unlimited first-come allocation, email-based allocation, and forcing all jobs onto one GPU are unfair or inefficient.

4. Question 4Security and governance

   A regulated workload must keep its data inside a private network with no access to public internet endpoints. Which design choice meets this?

   1. AAllow unrestricted outbound internet access from every node for updates
   2. BStore training data in a publicly readable cloud storage bucket with open ACLs
   3. CDisable network security controls and firewall rules on the training nodes
   4. DUse private networking and local model or container registries with access controls
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   Correct answer: D

   Private networking and locally hosted registries with access controls keep data and dependencies inside the boundary. Unrestricted internet access, public buckets, and disabled security expose the data.

5. Question 5Troubleshooting

   A distributed training job fails with a communication timeout between nodes. Which check is most useful first?

   1. AThe author's name in the code header and commit message history
   2. BThe number of asset-tag stickers applied to the server chassis during the last rack audit
   3. CThe colour of the job's log file header in the scheduler's web dashboard
   4. DNetwork health and connectivity between the nodes on the training fabric
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   Correct answer: D

   Communication timeouts usually point to network health or connectivity on the training fabric, so that is checked first. Log colour, stickers, and author names do not affect communication.