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NVIDIA NCP-AII Exam Syllabus Topics:

Section	Weight	Objectives
Control Plane Installation and Configuration	19%	- GPU utilization in containers 1. Docker GPU usage validation - Infrastructure software stack deployment 1. Cluster setup (Slurm, Enroot, Pyxis) 2. Operating system installation 3. Base Command Manager (BCM) installation and HA configuration - Drivers and toolkits 1. NGC CLI deployment 2. NVIDIA container toolkit installation 3. NVIDIA GPU and DOCA drivers installation/update
Cluster Test and Verification	33%	- Cluster diagnostics 1. Storage testing 2. ClusterKit multi-node assessment - Network and hardware validation 1. NVLink validation 2. Cabling and signal verification 3. Firmware validation (switches, transceivers, BlueField) - Performance and stress testing 1. HPL (High-Performance Linpack) benchmarking 2. Single-node stress testing 3. Cluster burn-in tests (HPL, NCCL, NeMo) 4. NCCL communication testing
Physical Layer Management	5%	- Networking and GPU partitioning 1. MIG (Multi-Instance GPU) configuration 2. BlueField network platform configuration
System and Server Bring-up	31%	- Hardware initialization and configuration 1. Firmware upgrades including HGX and fault detection 2. BMC, OOB, and TPM initial configuration 3. GPU server installation and validation 4. Hardware validation for workloads - Deployment and validation lifecycle 1. Sequence of deployment and validation events 2. Network topologies for AI factories - Physical infrastructure validation 1. Storage parameter initialization 2. Power and cooling validation 3. Cable and transceiver types validation
Troubleshoot and Optimize	12%	- Fault detection and remediation 1. GPU, fan, network card fault identification 2. Replacement of faulty hardware components - Performance optimization 1. Storage optimization 2. Server performance tuning (Intel/AMD platforms)

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NVIDIA AI Infrastructure Sample Questions (Q151-Q156):

NEW QUESTION # 151
In an InfiniBand fabric, what is the primary role of the Subnet Manager (SM) with respect to routing?

A. To discover the network topology, calculate routing paths, and program the forwarding tables (LID tables) in the switches.
B. To act as a firewall, blocking unauthorized traffic based on pre-defined rules.
C. To monitor the network for congestion and dynamically adjust packet priorities using Quality of Service (QOS) mechanisms.
D. To forward packets based on destination IP addresses, similar to a traditional IP router.
E. To provide a command-line interface for users to manually configure routing tables on each InfiniBand switch.

Answer: A

Explanation:
The Subnet Manager (SM) is responsible for discovering the InfiniBand topology, calculating routes, and programming the forwarding tables (LID tables) within the switches. This is crucial for establishing connectivity and ensuring efficient data transfer within the fabric. InfiniBand uses LID (Local Identifier) based routing, not IP addresses directly.

NEW QUESTION # 152
A systems engineer is updating firmware across a large DGX cluster using automation. What is the best practice for minimizing risk and ensuring cluster health during and after the process?

A. Drain nodes from the scheduler, run pre-update diagnostics, update firmware in batches, and verify health post-update before scaling to the next batch.
B. Update nodes that have reported faults, leaving others on older firmware.
C. Drain nodes from the scheduler, update firmware in batches, skip diagnostics and verify health post- update before scaling to the next batch.
D. To save time, simultaneously update all nodes in the cluster without draining or diagnostics.

Answer: A

Explanation:
Updating firmware on an NVIDIA DGX cluster is a critical operation that involves multiple sensitive components, including the GPU baseboard, the BMC, the motherboard tray (SBC), and the InfiniBand HCAs.
In a production environment, " Batching " is the industry standard to prevent a single corrupted firmware image or update failure from taking down the entire AI factory. The process must begin with " Draining " the nodes in the workload scheduler (like Slurm or Kubernetes) to ensure no active training jobs are interrupted.
Running pre-update diagnostics-using tools like nvsm show health or dcgmi diag-is vital to establish a baseline and ensure the hardware is stable before applying changes. Once the firmware is applied in a controlled batch, post-update verification is required to confirm the system returns to a " Healthy " state and that all versions match the target manifest. This " Rolling Update " strategy allows the engineer to pause the automation if a specific node fails to return to service, protecting the overall availability of the cluster.
Skipping diagnostics (Option D) or leaving nodes on mismatched versions (Option C) creates " configuration drift, " which leads to unpredictable performance in collective communication libraries.

NEW QUESTION # 153
A Slurm-managed AI cluster contains both H100 and L40 GPU nodes. Several inference jobs requiring only modest GPU resources are repeatedly scheduled onto H100 nodes, delaying large distributed training jobs. Which scheduling strategy would best improve overall cluster efficiency?

A. Disable scheduling on H100 nodes.
B. Configure every node with identical GPU memory.
C. Define partitions or resource constraints so workloads are matched to appropriate GPU types.
D. Allow Slurm to randomly assign GPUs without resource awareness.

Answer: C

Explanation:
Slurm supports partitions, Generic Resources (GRES), constraints, and scheduling policies that match workloads to suitable hardware. Assigning inference workloads to lower-cost GPUs while reserving H100 systems for large-scale training improves utilization and throughput. Disabling nodes wastes resources, and random scheduling ignores workload requirements.

NEW QUESTION # 154
An engineer wants to verify that their NVIDIA GPU is accessible inside a Docker container for running deep learning workloads. They have installed the NVIDIA Container Toolkit on a machine with working NVIDIA drivers. Which command demonstrates the correct way to run a container that can access all available GPUs?

A. docker run --rm it ubuntu:22.04 nvidia-smi
B. docker run --rm --gpus all nvidia/cuda:12.4.0-base-ubuntu22.04 nvidia-smi
C. docker run --rm --runtime=docker nvidia/cuda nvidia-smi
D. docker run --rm nvidia/cuda:12.4.0-base-ubuntu22.04 nvidia-smi

Answer: B

Explanation:
The --gpus all option exposes all available host GPUs to the Docker container through the NVIDIA Container Toolkit. Running nvidia-smi inside the CUDA container verifies that the container can see and access the GPUs correctly.

NEW QUESTION # 155
A system administrator needs to configure a BlueField DPU and enable RShim on the baseboard management controller (BMC). Which command should be executed?

A. ipmitool raw 0x32 0x6a 1
B. systemctl enable bmc-rshim.service
C. scp <path_to_bfb> root@<bmc_ip>:/dev/rshim0/boot</bmc_ip></path_to_bfb>
D. systemctl restart rshim

Answer: A

Explanation:
The ipmitool raw 0x32 0x6a 1 command enables RShim access through the BMC for BlueField DPU configuration. This allows the BMC to expose the RShim interface needed for DPU provisioning and management.

NEW QUESTION # 156
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