Questions for the NCP-AIO were updated on : Nov 21 ,2025
An organization has multiple containers and wants to view STDIN, STDOUT, and STDERR I/O streams
of a specific container.
What command should be used?
C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The docker logs CONTAINER-NAME command retrieves the standard output (STDOUT) and standard
error (STDERR) streams of a running or stopped container. It is the primary tool for inspecting
container I/O logs for debugging and monitoring purposes. docker top shows running processes,
docker stats shows resource usage, and docker inspect shows metadata/configuration.
You are managing a high-performance computing environment. Users have reported storage
performance degradation, particularly during peak usage hours when both small metadata-intensive
operations and large sequential I/O operations are being performed simultaneously. You suspect that
the mixed workload is causing contention on the storage system.
Which of the following actions is most likely to improve overall storage performance in this mixed
workload environment?
B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Separating metadata-intensive workloads and large sequential I/O operations onto different storage
pools isolates contention points and optimizes performance for each workload type. Metadata
operations benefit from dedicated resources optimized for small, random access, while large
sequential I/O requires high-throughput storage. This separation minimizes conflicts and improves
overall system responsiveness.
You are managing a deep learning workload on a Slurm cluster with multiple GPU nodes, but you
notice that jobs requesting multiple GPUs are waiting for long periods even though there are
available resources on some nodes.
How would you optimize job scheduling for multi-GPU workloads?
B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To optimize scheduling of multi-GPU jobs in Slurm, it is essential to correctly specify GPU requests in
job scripts using --gres=gpu:<number> and enable/configure Slurm’s backfill scheduler. Backfill
allows smaller jobs to run opportunistically in gaps without delaying larger multi-GPU jobs,
improving cluster utilization and reducing wait times for multi-GPU jobs. Proper configuration
ensures efficient packing and priority handling of GPU resources.
An administrator needs to submit a script named “my_script.sh” to Slurm and specify a custom
output file named “output.txt” for storing the job's standard output and error.
Which ‘sbatch’ option should be used?
A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct sbatch option to specify a custom output file for both standard output and error is -o
output.txt (or --output=output.txt). This option directs Slurm to write the job’s standard output and
error streams to the specified file. The -e option is for standard error only, and -output-output is not a
valid option.
You are monitoring the resource utilization of a DGX SuperPOD cluster using NVIDIA Base Command
Manager (BCM). The system is experiencing slow performance, and you need to identify the cause.
What is the most effective way to monitor GPU usage across nodes?
B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The Base View dashboard in NVIDIA Base Command Manager provides a centralized and real-time
overview of GPU, CPU, and memory utilization across all nodes in the DGX SuperPOD cluster. This
tool allows administrators to quickly identify bottlenecks and resource usage patterns efficiently,
unlike manually checking logs or running commands node-by-node.
An instance of NVIDIA Fabric Manager service is running on an HGX system with KVM. A System
Administrator is troubleshooting NVLink partitioning.
By default, what is the GPU polling subsystem set to?
B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In NVIDIA AI infrastructure, the NVIDIA Fabric Manager service is responsible for managing GPU
fabric features such as NVLink partitioning on HGX systems. This service periodically polls the GPUs
to monitor and manage NVLink states. By default, the GPU polling subsystem is set to every 30
seconds to balance timely updates with system resource usage.
This polling interval allows the Fabric Manager to efficiently detect and respond to changes or issues
in the NVLink fabric without excessive overhead or latency. It is a standard default setting unless
specifically configured otherwise by system administrators.
This default behavior aligns with NVIDIA’s system management guidelines for HGX platforms and is
referenced in NVIDIA AI Operations materials concerning fabric management and troubleshooting of
NVLink partitions.
An administrator is troubleshooting issues with an NVIDIA Unified Fabric Manager Enterprise (UFM)
installation and notices that the UFM server is unable to communicate with InfiniBand switches.
What step should be taken to address the issue?
D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Communication issues between UFM server and InfiniBand switches often result from misconfigured
or missing subnet manager configuration on the switches. The subnet manager controls fabric
membership and routing, so verifying and correcting its setup is essential for proper UFM operation.
Rebooting, adding GPUs, or disabling firewalls are less likely to resolve fabric-level communication
problems.
What two (2) platforms should be used with Fabric Manager? (Choose two.)
A, D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIA Fabric Manager is designed to manage and optimize fabric resources like NVLink and
NVSwitch in enterprise-class platforms such as HGX and DGX systems. These platforms have the
necessary hardware fabric components. The L40S Certified and GeForce series are either not
compatible or do not require Fabric Manager.
What should an administrator check if GPU-to-GPU communication is slow in a distributed system
using Magnum IO?
D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Slow GPU-to-GPU communication in distributed systems often relates to the configuration of
communication libraries such as NCCL (NVIDIA Collective Communications Library) or NVSHMEM.
Ensuring these libraries are properly configured and optimized is critical for efficient GPU
communication. Limiting GPUs or increasing RAM does not directly improve communication speed,
and disabling InfiniBand would degrade performance.
A system administrator needs to lower latency for an AI application by utilizing GPUDirect Storage.
What two (2) bottlenecks are avoided with this approach? (Choose two.)
B, D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
GPUDirect Storage allows data to be transferred directly from storage to GPU memory, bypassing the
CPU and system memory. This reduces latency and overhead by avoiding data movement through
the CPU and main memory, accelerating data feeding to GPUs for AI workloads. PCIe and NIC are still
involved in the data path, and the DPU may participate depending on architecture but are not the
primary bottlenecks avoided by GPUDirect Storage.
A system administrator notices that jobs are failing intermittently on Base Command Manager due to
incorrect GPU configurations in Slurm. The administrator needs to ensure that jobs utilize GPUs
correctly.
How should they troubleshoot this issue?
B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Misconfiguration related to MIG mode can cause Slurm to improperly allocate GPUs, leading to job
failures. The administrator should verify whether MIG has been enabled on the GPUs and ensure
that Slurm’s configuration matches the hardware setup. If MIG is enabled, Slurm must be configured
to recognize and schedule MIG partitions correctly to avoid resource conflicts.
A system administrator is troubleshooting a Docker container that is repeatedly failing to start. They
want to gather more detailed information about the issue by generating debugging logs.
Why would generating debugging logs be an important step in resolving this issue?
B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Generating debugging logs enables detailed visibility into the internal operations of the Docker
daemon. These logs expose low-level errors, misconfigurations, and runtime issues that standard
logs might not capture, making them essential for diagnosing why a container repeatedly fails to
start.
What steps should an administrator take if they encounter errors related to RDMA (Remote Direct
Memory Access) when using Magnum IO?
C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Since Magnum IO relies on RDMA for direct data paths between storage and compute nodes,
encountering RDMA errors requires verifying that RDMA is enabled and correctly configured on all
involved nodes. This includes checking the network fabric, firmware versions, drivers, and ensuring
compatibility. Disabling RDMA or unnecessary reboots do not solve underlying configuration
problems.
If a Magnum IO-enabled application experiences delays during the ETL phase, what troubleshooting
step should be taken?
D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Ensuring that GPUDirect Storage is properly configured allows the application to transfer data
directly from storage into GPU memory, bypassing the CPU and reducing latency and overhead
during the ETL (Extract, Transform, Load) phase. This direct path optimizes data movement,
preventing delays and improving performance for Magnum IO-enabled applications.
A GPU administrator needs to virtualize AI/ML training in an HGX environment.
How can the NVIDIA Fabric Manager be used to meet this demand?
C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIA Fabric Manager manages the NVLink and NVSwitch fabric resources within HGX systems,
enabling efficient resource allocation, communication, and virtualization necessary for AI/ML
workloads. This is critical for virtualization as it ensures optimized interconnect performance
between GPUs. Video encoding, graphical rendering, or memory upgrades are outside the scope of
Fabric Manager.