Quiz Updated NCP-AII - NVIDIA AI Infrastructure Valid Exam Labs

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NVIDIA NCP-AII Exam Syllabus Topics:

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

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NVIDIA AI Infrastructure Sample Questions (Q13-Q18):

NEW QUESTION # 13
A system administrator needs to enable MIG so that the end user can run multiple jobs on an NVIDIA A100 GPU. What command should be used?

A. nvidia smi -i 0 --enablemig
B. nvidia -smi -i -mig 1 enable
C. nvidia-smi -a100 -enablemig
D. nvidia-smi -i 0 -mig 1

Answer: D

Explanation:
nvidia-smi -i 0 -mig 1 enables MIG mode on GPU index 0. Once MIG mode is enabled, the A100 can be partitioned into multiple GPU instances so separate jobs can run with isolated GPU resources.

NEW QUESTION # 14
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 --gpus all nvidia/cuda:12.4.0-base-ubuntu22.04 nvidia-smi
B. docker run --rm --runtime=docker nvidia/cuda nvidia-smi
C. docker run --rm nvidia/cuda:12.4.0-base-ubuntu22.04 nvidia-smi
D. docker run --rm it ubuntu:22.04 nvidia-smi

Answer: A

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 # 15
You are using MIG (Multi-lnstance GPU) on an NVIDIAAI 00 GPU within a Kubernetes cluster. You want to configure a pod to use a specific MIG instance. How do you define the GPU resource request in the pod's YAML definition?

A. Use a custom resource definition (CRD) to define MIG instances and reference it in the resource request.
B. Using the annotation 'nvidia.com/gpu.product' and the value of the particular GPU device you desire.
C. Specify the full GPU ID (e.g., 'nvidia.com/gpu: 1') in the resource request.
D. Specify the MIG device profile name (e.g., 'nvidia.com/mig-l g.7gb: 1 in the resource request.
E. Use a generic resource request like 'nvidia.com/gpu: 0.125' to allocate a portion of the GPIJ.

Answer: D

Explanation:
When using MIG, you need to specify the MIG device profile name in the resource request to allocate a specific MIG instance to the pod. For example, 'nvidia.com/mig-1g.7gb: 1' requests one instance of the lg.7gb MIG profile. Specifying the full GPU ID will not work with MIG. Generic resource requests do not work with MIG.

NEW QUESTION # 16
You are tasked with optimizing an Intel Xeon scalable processor-based server running a TensorFlow model with multiple NVIDIA GPUs.
You observe that the CPU utilization is low, but the GPU utilization is also not optimal. The profiler shows significant time spent in 'tf.data' operations. Which of the following actions would MOST likely improve performance?

A. Reduce the global batch size to improve memory utilization.
B. Use 'tf.data.AUTOTIJNE to allow TensorFlow to dynamically optimize the data pipeline.
C. Upgrade the server's network adapter to a faster interface, such as 100Gb
D. Enable XLA (Accelerated Linear Algebra) compilation in TensorFlow.
E. Increase the number of threads used for CPU-bound operations in TensorFlow using 'tf.config.threading.set_intra_op_parallelism_threads()'.

Answer: B

Explanation:
'tf.data' performance issues often stem from inefficient data pipelines. 'tf.data.AIJTOTUNE allows TensorFlow to dynamically optimize the pipeline by adjusting parameters such as prefetch buffer size and the number of parallel calls to transformation functions. XLA compilation optimizes graph execution, but 'tf.data' issues need to be addressed first. Increasing CPU threads might help but 'AUTOTUNE is more specific to the problem. A smaller batch size could negatively impact GPU utilization. Network upgrades are irrelevant as the problem lies within the server.

NEW QUESTION # 17
You must validate all physical cabling as part of the network bring-up phase in a new NVIDIA GPU cluster deployment. The design requires you to confirm that each cable matches the intended topology, all links are functional, and future troubleshooting and scalability are supported. Which two steps are essential to an effective recommended cabling validation process during cluster deployment?
Pick the 2 correct responses below.

A. Run the cable validation process incrementally during deployment, section by section, to catch and resolve errors as early as possible.
B. Run link tests only after the entire network is built and powered on to avoid redundant troubleshooting during bring-up.
C. Focus on validating the highest bandwidth links first, deferring non-critical cable mislabeling until after initial workloads are deployed and tested.
D. Compare every cable's physical connection to the planned topology diagram and validate correct ports and link paths.

Answer: A,D

Explanation:
The correct steps are incremental validation and comparison against the planned topology. In NVIDIA AI infrastructure, especially DGX BasePOD, DGX SuperPOD, InfiniBand, or Spectrum-X deployments, cabling accuracy is not a cosmetic requirement. The physical topology determines rail alignment, path diversity, routing behavior, bandwidth consistency, and troubleshooting reliability. Validating section by section during deployment prevents large-scale rework because errors are discovered while the affected rack, switch, or node group is still being installed. Waiting until the full fabric is complete can make fault isolation much harder.
Comparing every physical connection to the topology diagram ensures that each HCA, switch port, transceiver, and cable is connected to the intended neighbor. Wrong-port cabling can still produce link-up status, but it may break expected topology, create congestion, or reduce performance during NCCL and RDMA workloads. Deferring "minor" labeling or port errors is risky because AI fabrics depend on deterministic design. Management pings alone are also insufficient because they do not validate high-speed compute fabric cabling.

NEW QUESTION # 18
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