NVIDIA NCP-AII: NVIDIA-Certified Professional: AI Infrastructure

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

1. Question 1Networking

   An InfiniBand fabric shows lower than expected throughput between two nodes. Which check is most useful first?

   1. AConfirm the link speed and width on both ports and check for errors with the InfiniBand diagnostics tools
   2. BRestart the node's keyboard driver stack and reseat the USB console adapter used for local login at the front panel
   3. CIncrease the screen resolution on the admin workstation to 4K and recalibrate the monitor's refresh rate and color profile
   4. DChange the hostname of the switch and update the SNMP community strings so that the monitoring dashboards label the fabric correctly
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   Correct answer: A

   Link speed, width and error counters quickly show whether a port is negotiating correctly or has errors. Keyboards, hostnames and screen resolution do not affect fabric throughput.

2. Question 2Cluster design and deployment

   A 32-node DGX cluster will run distributed training with heavy all-reduce traffic. The architect must choose the compute fabric. Which design gives the lowest latency and highest bandwidth for GPU-to-GPU communication across nodes?

   1. AWi-Fi 6E bridges between racks using 5 GHz access points as the inter-node transport
   2. BLegacy RS-232 serial links daisy-chained between nodes for passing collective data
   3. CA dedicated InfiniBand or Spectrum-X fabric with rail-optimized topology for the compute network
   4. DA single 1 GbE management network carrying every class of traffic: training gradients, storage I/O, and NCCL collectives
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   Correct answer: C

   A dedicated high-bandwidth fabric with rail-optimized topology keeps collective traffic fast and predictable. Management networks and wireless or serial links cannot sustain training traffic.

3. Question 3Performance and troubleshooting

   A training job runs slower on some nodes than others, even though the nodes have the same hardware. Which check is most likely to reveal the cause?

   1. ACompare the colour of the server front panels and the bezel stickers on each rack
   2. BCompare the names of the users logged in to each node's console and the shell history of each session
   3. CCompare the number of open browser tabs on the administrator's workstation during the run
   4. DCompare GPU clocks, temperature, and throttling events across the nodes using monitoring data
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   Correct answer: D

   Throttling, temperature and clock differences often explain inconsistent node performance. Physical colour, user names and browser tabs do not affect GPU performance.

4. Question 4Storage

   A training job reads a large dataset and the GPUs spend most of their time idle waiting for data. What is the most likely bottleneck to investigate?

   1. AThe GPU clock speed setting configured by the driver power-management policy
   2. BStorage read throughput and the path from the parallel file system to the nodes
   3. CThe model's name and its tokenizer version listed in the training config file
   4. DThe keyboard on the admin console plugged into the head node, which has a worn key switch
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   Correct answer: B

   GPUs waiting for data points to storage or I/O throughput. Clock speed is not the limit when GPUs are idle, and the keyboard and names are unrelated.

5. Question 5Security

   An administrator must restrict which users can submit jobs to a GPU cluster and track their usage. Which approach is most appropriate?

   1. AAnonymous job submission via an open endpoint, where jobs run under a generic guest account with no user identity checked
   2. BDisabling job accounting to protect user privacy, so the scheduler stops tracking GPU hours per account
   3. COne shared login account for every engineer, with the same credentials passed around the team
   4. DRole-based access control in the scheduler or cluster manager, with per-user accounts and usage accounting
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   Correct answer: D

   RBAC with per-user accounts and accounting controls access and tracks usage. Shared or anonymous access removes accountability, and disabling accounting prevents tracking that the team needs.