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NVIDIA NCP-AIO Sample Question Answers
Question # 1
A system administrator needs to lower latency for an AI application by utilizing GPUDirectStorage.What two (2) bottlenecks are avoided with this approach? (Choose two.)
A. PCIe B. CPU C. NIC D. System Memory E. DPU
Answer: B,D
Explanation:
GPUDirect Storage enables a direct data path between storage and GPU memory,
bypassing the CPU and system memory entirely. Normally data would have to travel
through system memory and be managed by the CPU before reaching the GPU, both of
which introduce latency and consume resources. GPUDirect Storage eliminates these two
bottlenecks, resulting in faster, more efficient data transfers for AI workloads.
Question # 2
An administrator needs to submit a script named “my_script.sh” to Slurm and specify acustom output file named “output.txt” for storing the job's standard output and error.Which „sbatch? option should be used?
A. =-o output.txt B. =-e output.txt C. =-output-output output.txt
Answer: A
Explanation:
In Slurm Workload Manager, the sbatch option -o (or --output) is used to define the file where
standard output and standard error of the job will be written. So using -o output.txt directs all
job logs into that file.
Why not others?
? B (-e) ? only sets the error file separately, not both output and error
? C ? invalid syntax
So the correct and standard option is -o output.txt
Question # 3
An organization has multiple containers and wants to view STDIN, STDOUT, and STDERRI/O streams of a specific container.What command should be used?
A. docker top CONTAINER-NAME B. docker stats CONTAINER-NAME C. docker logs CONTAINER-NAME D. docker inspect CONTAINER-NAME
Answer: C
Explanation:
docker logs captures and displays the STDIN, STDOUT, and STDERR streams of a
container, making it the right tool for viewing a container's I/O output. docker top shows
You are a Solutions Architect designing a data center infrastructure for a cloud-based AIapplication that requires high-performance networking, storage, and security. You need tochoose a software framework to program the NVIDIA BlueField DPUs that will be used inthe infrastructure. The framework must support the development of custom applicationsand services, as well as enable tailored solutions for specific workloads. Additionally, theframework should allow for the integration of storage services such as NVMe over Fabrics(NVMe-oF) and elastic block storage.Which framework should you choose?
A. NVIDIA TensorRT B. NVIDIA CUDA C. NVIDIA NSight D. NVIDIA DOCA
Answer: D
Explanation:
NVIDIA DOCA (Data Center Infrastructure on a Chip Architecture) is the dedicated software
framework for programming BlueField DPUs. It provides a comprehensive set of libraries and APIs
for building custom networking, storage, and security applications directly on the DPU. Critically, it
supports NVMe-oF and elastic block storage integration, which are specifically called out in the
requirements.
The other options don't fit. TensorRT is for deep learning inference optimization, CUDA is for GPUaccelerated computing, and NSight is a performance profiling tool. None of these are designed for
DPU programming or infrastructure offloading.
Question # 5
A system administrator is experiencing issues with Docker containers failing to start due tovolume mounting problems. They suspect the issue is related to incorrect file permissionson shared volumes between the host and containers.How should the administrator troubleshoot this issue?
A. Use the docker logs command to review the logs for error messages related to volume
mounting and permissions. B. Reinstall Docker to reset all configurations and resolve potential volume mounting
issues. C. Disable all shared folders between the host and container to prevent volume mounting
errors. D. Reduce the size of the mounted volumes to avoid permission conflicts during container
startup.
Answer: A
Explanation:
docker logs is the right first step here. It surfaces the exact error messages thrown when the
container fails to start, giving the administrator clear visibility into what permission or
mounting issue is occurring. Reinstalling Docker or disabling shared folders are destructive
approaches that don't diagnose anything, and reducing volume size has no bearing on
permission problems.
Question # 6
A Slurm user is experiencing a frequent issue where a Slurm job is getting stuck in the“PENDING” state and unable to progress to the “RUNNING” state.Which Slurm command can help the user identify the reason for the job?s pending status?
A. sinfo -R B. scontrol show job <jobid> C. sacct -j <job[.step]> D. squeue -u <user_list>
Answer: B
Explanation:
scontrol show job provides detailed information about a specific job including the Reason
field, which directly tells you why the job is stuck in PENDING state, whether it's waiting for
resources, hitting a partition limit, or held for another reason. The other commands offer
useful cluster and job information but none pinpoint the pending reason as directly as this
one.
Question # 7
If a Magnum IO-enabled application experiences delays during the ETL phase, whattroubleshooting step should be taken?
A. Disable NVLink to prevent conflicts between GPUs during data transfer. B. Reduce the size of datasets being processed by splitting them into smaller chunks. C. Increase the swap space on the host system to handle larger datasets. D. Ensure that GPUDirect Storage is configured to allow direct data transfer from storage
to GPU memory.
Answer: D
Explanation:
ETL phase delays in a Magnum IO application typically point to a data transfer bottleneck.
GPUDirect Storage eliminates the CPU from the data path by allowing storage to transfer
directly into GPU memory, which is exactly what Magnum IO is designed to leverage. If it's
not properly configured, data has to take the slower route through system memory, causing
the kind of delays described.
Question # 8
You are deploying AI applications at the edge and want to ensure they continue runningeven if one of the servers at an edge location fails.How can you configure NVIDIA Fleet Command to achieve this?
A. Use Secure NFS support for data redundancy. B. Set up over-the-air updates to automatically restart failed applications. C. Enable high availability for edge clusters. D. Configure Fleet Command's multi-instance GPU (MIG) to handle failover.
Answer: C
Explanation:
Fleet Command has a built-in high availability option for edge clusters that ensures
applications keep running even if a server fails. The other options serve different purposes
— NFS is for storage, OTA updates are for software deployment, and MIG is for GPU
partitioning, none of which address server-level failover.
Question # 9
An administrator requires full access to the NGC Base Command Platform CLI.Which command should be used to accomplish this action?
A. ngc set API B. ngc config set C. ngc config BCP
Answer: B
Explanation:
ngc config set is the command used to configure the NGC CLI with your API key and other
settings, granting full authenticated access to the NGC Base Command Platform.
Question # 10
You are an administrator managing a large-scale Kubernetes-based GPU cluster usingRun:AI.To automate repetitive administrative tasks and efficiently manage resources acrossmultiple nodes, which of the following is essential when using the Run:AI Administrator CLIfor environments where automation or scripting is required?
A. Use the runai-adm command to directly update Kubernetes nodes without requiring
kubectl. B. Use the CLI to manually allocate specific GPUs to individual jobs for better resource
management. C. Ensure that the Kubernetes configuration file is set up with cluster administrative rights
before using the CLI. D. Install the CLI on Windows machines to take advantage of its scripting capabilities.
Answer: C
Explanation:
The Run:AI Administrator CLI operates on top of Kubernetes, so it requires a properly
configured kubeconfig file with cluster-level administrative rights before any automation or
scripting can work. Without the right permissions in place, CLI commands simply won't have
the access needed to manage resources across the cluster.
Question # 11
You have noticed that users can access all GPUs on a node even when they request onlyone GPU in their job script using --gres=gpu:1. This is causing resource contention andinefficient GPU usage.What configuration change would you make to restrict users? access to only their allocatedGPUs?
A. Increase the memory allocation per job to limit access to other resources on the node. B. Enable cgroup enforcement in cgroup.conf by setting ConstrainDevices=yes. C. Set a higher priority for Jobs requesting fewer GPUs, so they finish faster and free up
resources sooner. D. Modify the job script to include additional resource requests for CPU cores alongside
GPUs.
Answer: B
Explanation:
Setting ConstrainDevices=yes in Slurm's cgroup configuration enforces hardware-level
access control, ensuring users can only access the specific GPUs allocated to their job.
Without this, Slurm allocates GPUs logically but doesn't physically restrict access, allowing
processes to see and potentially use GPUs outside their allocation.
Question # 12
After completing the installation of a Kubernetes cluster on your NVIDIA DGX systemsusing BCM, how can you verify that all worker nodes are properly registered and ready?
A. Run kubectl get nodes to verify that all worker nodes show a status of “Ready”. B. Run kubectl get pods to check if all worker pods are running as expected. C. Check each node manually by logging in via SSH and verifying system status with
systemctl.
Answer: A
Explanation:
This is the standard and most direct way to confirm node registration in a Kubernetes
cluster. It instantly shows all nodes and their current status in one command.
Question # 13
An administrator is troubleshooting issues with NVIDIA GPUDirect storage and mustensure optimal data transfer performance.What step should be taken first?
A. Increase the GPU's core clock frequency. B. Upgrade the CPU to a higher clock speed. C. Check for compatible RDMA-capable network hardware and configurations. D. Install additional GPU memory (VRAM).
Answer: C
Explanation:
GPUDirect Storage relies on RDMA to enable direct data transfers between storage and
GPU memory, bypassing the CPU entirely. If RDMA-capable hardware isn't present or
properly configured, GPUDirect Storage simply won't function correctly regardless of any
other optimizations. This makes it the logical first step before looking at anything else.
Question # 14
You are monitoring the resource utilization of a DGX SuperPOD cluster using NVIDIA BaseCommand Manager (BCM). The system is experiencing slow performance, and you need toidentify the cause.What is the most effective way to monitor GPU usage across nodes?
A. Check the job logs in Slurm for any errors related to resource requests. B. Use the Base View dashboard to monitor GPU, CPU, and memory utilization in real time C. Run the top command on each node to check CPU and memory usage. D. Use nvidia-smi on each node to monitor GPU utilization manually.
Answer: B
Explanation:
BCM's Base View dashboard provides a centralized, real-time view of GPU, CPU, and
memory utilization across all nodes in the cluster. Running nvidia-smi or top manually on
each node is inefficient at scale, and checking Slurm job logs only reveals errors after the fact
rather than giving live resource visibility across the entire SuperPOD.
Question # 15
You are managing multiple edge AI deployments using NVIDIA Fleet Command. You needto ensure that each AI application running on the same GPU is isolated from others toprevent interference.Which feature of Fleet Command should you use to achieve this?
A. Remote Console B. Secure NFS support C. Multi-Instance GPU (MIG) support D. Over-the-air updates
Answer: C
Explanation:
Fleet Command's MIG support allows you to partition a single GPU into isolated instances,
each with dedicated memory and compute resources. This ensures AI applications running
on the same GPU cannot interfere with each other, which is exactly what's needed in a
multi-tenant edge deployment.
Question # 16
What steps should an administrator take if they encounter errors related to RDMA (RemoteDirect Memory Access) when using Magnum IO?
A. Increase the number of network interfaces on each node to handle more trafficconcurrently without using RDMA. B. Disable RDMA entirely and rely on TCP/IP for all network communications between
nodes. C. Check that RDMA is properly enabled and configured on both storage and computenodes for efficient data transfers. D. Reboot all compute nodes after every job completion to reset RDMA settings
automatically.
Answer: C
Explanation:
RDMA errors typically stem from misconfiguration on one or both ends of the connection.
Since Magnum IO relies on RDMA for high-speed, low-latency data transfers between
storage and compute nodes, verifying that RDMA is correctly enabled and configured on both
sides is the most direct and logical troubleshooting step. Disabling RDMA or rebooting nodes
are counterproductive approaches that don't solve the underlying issue.
Question # 17
A system administrator needs to optimize the delivery of their AI applications to the edge.What NVIDIA platform should be used?
A. Base Command Platform B. Base Command Manager C. Fleet Command D. NetQ
Answer: C
Explanation:
NVIDIA Fleet Command is specifically designed for deploying and managing AI applications
at the edge, providing centralized control over distributed edge locations. Base Command
Platform and Manager are focused on data center cluster management, and NetQ is a
network monitoring tool for Cumulus Linux environments.
Question # 18
You are deploying an AI workload on a Kubernetes cluster that requires access to GPUsfor training deep learning models. However, the pods are not able to detect the GPUs onthe nodes.What would be the first step to troubleshoot this issue?
A. Verify that the NVIDIA GPU Operator is installed and running on the cluster. B. Ensure that all pods are using the latest version of TensorFlow or PyTorch. C. Check if the nodes have sufficient memory allocated for AI workloads. D. Increase the number of CPU cores allocated to each pod to ensure better resource
utilization.
Answer: A
Explanation:
The NVIDIA GPU Operator is what enables Kubernetes to detect and manage GPUs on
nodes. It automatically deploys all the necessary components including drivers, the device
plugin, and container runtime configuration. If pods can't detect GPUs, this is the first and
most logical place to check since everything else depends on it being properly installed and
running.
Question # 19
A Slurm user needs to submit a batch job script for execution tomorrow.Which command should be used to complete this task?
A. sbatch -begin=tomorrow B. submit -begin=tomorrow C. salloc -begin=tomorrow D. srun -begin=tomorrow
Answer: A
Explanation:
sbatch is the correct Slurm command for submitting batch job scripts, and the --begin flag
allows scheduling it for a future time. The other options don't apply here as salloc is for
interactive resource allocation, srun launches jobs directly, and submit is not a valid Slurm
command.
Question # 20
A system administrator is troubleshooting a Docker container that is repeatedly failing tostart. They want to gather more detailed information about the issue by generatingdebugging logs.Why would generating debugging logs be an important step in resolving this issue?
A. Debugging logs disable other logging mechanisms, reducing noise in the output. B. Debugging logs provide detailed insights into the Docker daemon's internal operations. C. Debugging logs prevent the container from being removed after it stops, allowing for
easier inspection. D. Debugging logs fix issues related to container performance and resource allocation.
Answer: B
Explanation:
When a container repeatedly fails to start, the standard logs often don't reveal enough about
what's going wrong under the hood. Enabling debug-level logging exposes the Docker
daemon's internal operations, including detailed error traces, configuration issues, and
runtime failures that wouldn't appear in normal logs. This makes it much easier to pinpoint
the exact cause of the failure rather than guessing.
Question # 21
In a high availability (HA) cluster, you need to ensure that split-brain scenarios are avoided.What is a common technique used to prevent split-brain in an HA cluster?
A. Configuring manual failover procedures for each node. B. Using multiple load balancers to distribute traffic evenly across nodes. C. Implementing a heartbeat network between cluster nodes to monitor their health. D. Replicating data across all nodes in real time.
Answer: C
Explanation:
A heartbeat network allows nodes to continuously communicate and confirm each other's
status. If a node stops receiving heartbeats, it knows the other node is down and can safely
take over without both nodes assuming control simultaneously, which is exactly what causes
a split-brain scenario.
Question # 22
You need to do maintenance on a node. What should you do first?
A. Drain the compute node using scontrol update. B. Set the node state to down in Slurm before completing maintenance. C. Set the node state to down in Slurm before completing maintenance. D. Disable job scheduling on all compute nodes in Slurm before completing maintenance.
Answer: A
Explanation:
Draining the node with scontrol update NodeName=<node> State=DRAIN
Reason="maintenance" stops new jobs from being scheduled on it while letting running jobs finish
naturally. This is the safest approach before maintenance as it avoids abruptly killing active workloads.
Setting the node directly to DOWN kills running jobs immediately, and disabling scheduling clusterwide is unnecessary when only one node needs maintenance.
Question # 23
You are managing a high availability (HA) cluster that hosts mission-critical applications.One of the nodes in the cluster has failed, but the application remains available to users.What mechanism is responsible for ensuring that the workload continues to run withoutinterruption?
A. Load balancing across all nodes in the cluster. B. Manual intervention by the system administrator to restart services. C. The failover mechanism that automatically transfers workloads to a standby node. D. Data replication between nodes to ensure data integrity.
Answer: C
Explanation:
In an HA cluster, automatic failover is the core mechanism that detects node failure and
immediately shifts workloads to a standby node, keeping applications available without any
interruption or manual involvement.
Question # 24
Your organization is running multiple AI models on a single A100 GPU using MIG in amulti-tenant environment. One of the tenants reports a performance issue, but you noticethat other tenants are unaffected.What feature of MIG ensures that one tenant's workload does not impact others?
A. Hardware-level isolation of memory, cache, and compute resources for each instance. B. Dynamic resource allocation based on workload demand. C. Shared memory access across all instances. D. Automatic scaling of instances based on workload size.
Answer: A
Explanation:
MIG partitions the GPU at the hardware level, giving each instance its own dedicated
memory, cache, and compute resources. This is precisely why one tenant experiencing
issues doesn't affect others — there is no resource sharing between instances.
Question # 25
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 thoughthere are available resources on some nodes.How would you optimize job scheduling for multi-GPU workloads?
A. Reduce memory allocation per job so more jobs can run concurrently, freeing upresources faster for multi-GPU workloads. B. Ensure that job scripts use --gres=gpu: and configure Slurm’s backfill
scheduler to prioritize multi-GPU jobs efficiently. C. Set up separate partitions for single-GPU and multi-GPU jobs to avoid resource conflicts
between them. D. Increase time limits for smaller jobs so they don’t interfere with multi-GPU job
scheduling.
Answer: B
Explanation:
The --gres=gpu:<number> flag explicitly tells Slurm how many GPUs a job needs, and the
backfill scheduler intelligently fills gaps in the schedule by running smaller jobs without
delaying higher-priority multi-GPU jobs. Together they ensure GPU resources are correctly
requested and efficiently allocated, reducing unnecessary wait times.
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