[Q18-Q38] Exam Passing Guarantee Jan 09, 2026 NCP-AIO Exam with Accurate Quastions!

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Exam Passing Guarantee Jan 09, 2026 NCP-AIO Exam with Accurate Quastions!

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NVIDIA NCP-AIO Exam Syllabus Topics:

Topic	Details
Topic 1	Troubleshooting and Optimization: NVIThis section of the exam measures the skills of AI infrastructure engineers and focuses on diagnosing and resolving technical issues that arise in advanced AI systems. Topics include troubleshooting Docker, the Fabric Manager service for NVIDIA NVlink and NVSwitch systems, Base Command Manager, and Magnum IO components. Candidates must also demonstrate the ability to identify and solve storage performance issues, ensuring optimized performance across AI workloads.
Topic 2	Installation and Deployment: This section of the exam measures the skills of system administrators and addresses core practices for installing and deploying infrastructure. Candidates are tested on installing and configuring Base Command Manager, initializing Kubernetes on NVIDIA hosts, and deploying containers from NVIDIA NGC as well as cloud VMI containers. The section also covers understanding storage requirements in AI data centers and deploying DOCA services on DPU Arm processors, ensuring robust setup of AI-driven environments.
Topic 3	Administration: This section of the exam measures the skills of system administrators and covers essential tasks in managing AI workloads within data centers. Candidates are expected to understand fleet command, Slurm cluster management, and overall data center architecture specific to AI environments. It also includes knowledge of Base Command Manager (BCM), cluster provisioning, Run.ai administration, and configuration of Multi-Instance GPU (MIG) for both AI and high-performance computing applications.
Topic 4	Workload Management: This section of the exam measures the skills of AI infrastructure engineers and focuses on managing workloads effectively in AI environments. It evaluates the ability to administer Kubernetes clusters, maintain workload efficiency, and apply system management tools to troubleshoot operational issues. Emphasis is placed on ensuring that workloads run smoothly across different environments in alignment with NVIDIA technologies.

NEW QUESTION 18
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?

Reduce memory allocation per job so more jobs can run concurrently, freeing up resources faster for multi-GPU workloads.

Ensure that job scripts use –gres=gpu:<number> and configure Slurm’s backfill scheduler to prioritize multi-GPU jobs efficiently.

Set up separate partitions for single-GPU and multi-GPU jobs to avoid resource conflicts between them.

Increase time limits for smaller jobs so they don’t interfere with multi-GPU job scheduling.

NEW QUESTION 19
When deploying a DOCA application using the command, which of the following parameters are mandatory for specifying the DOCA core application’s entry point?

-entry-point Specifies the name of the function within the DOCA application that should be executed as the entry point.

‘-pci-address Defines the PCl address of the BlueField DPLJ where the application will run.

-num-cores Specifies the number of CPU cores to be allocated to the DOCA application.

‘-log-level ‘ : Sets the verbosity level for logging messages from the DOCA application.

‘-class The DOCAApplication class in the DOCA executable

NEW QUESTION 20
You are deploying a new AI model that requires very low latency inter-GPU communication. You have an NVSwitch-based system, and nvsm’ is managing the fabric. You suspect that the default ‘nvsm’ configuration might not be optimal for low-latency workloads. What advanced ‘nvsm’ configuration options or related system settings could you investigate to further minimize NVLink latency? Describe at least TWO specific areas you would explore.

Adjusting the NVLink power management settings to favor performance over power savings.

Tuning the system’s CPU frequency scaling governor.

Configuring Quality of Service (QOS) settings on the NVSwitch to prioritize NVLink traffic.

Disabling Address Space Layout Randomization (ASLR) for the application.

Disabling prefetching on the CPU.

NEW QUESTION 21
A system administrator is troubleshooting a Docker container that crashes unexpectedly due to a segmentation fault. They want to generate and analyze core dumps to identify the root cause of the crash.
Why would generating core dumps be a critical step in troubleshooting this issue?

Core dumps prevent future crashes by stopping any further execution of the faulty process.

Core dumps provide real-time logs that can be used to monitor ongoing application performance.

Core dumps restore the process to its previous state, often fixing the error-causing crash.

Core dumps capture the memory state of the process at the time of the crash.

NEW QUESTION 22
Which of the following are key considerations when selecting storage for an AI training workload using large datasets? (Select TWO)

Storage capacity.

IOPS (Input/Output Operations Per Second).

CPU core count on the storage server.

RAM on the storage server.

Network bandwidth between storage and compute nodes.

NEW QUESTION 23
A BCM pipeline is failing with ‘CUDA out of memory’ errors, even though “nvidia-smi’ reports available GPU memory. What steps should you take to diagnose and resolve this issue?

Reduce the batch size in the BCM pipeline configuration.

Enable CUDA memory pooling within the BCM framework.

Increase the shared memory allocation for the BCM pipeline.

Upgrade the GPU driver to the latest version.

A, B and C

NEW QUESTION 24
Consider this YAML snippet for deploying the NVIDIA device plugin. Which statement is true about the highlighted segment?

It will make sure that the device plugin gets deployed on nodes with ‘accelerator: nvidia-tesla-t4’ label

It will make sure that only tolerations are set, but there’s no affinity.

It’s a deprecated way of defining affinities. Consider using nodeAffinity instead.

It will make sure that the node affinity is ignored during scheduling.

This will ensure that the pod is only deployed on the node with certain taints, but with no other scheduling requirements

NEW QUESTION 25
You have a Run.ai cluster with multiple GPU nodes. You want to configure a specific job to ONLY run on nodes equipped with NVIDIA A100 GPUs. How can you achieve this node selection using Run.ai?

Use Kubernetes taints and tolerations to restrict the job to A100 nodes.

Configure node affinity rules in the Run.ai job definition to target nodes with the ‘nvidia.com/gpu.product’ label equal to ‘A1 00’.

Specify the A100 GPU type in the Run.ai cluster configuration.

Manually schedule the job on a specific AIOO node using the Run.ai CLI.

Use Run.ai’s built-in ‘gpu-type’ parameter in the job definition.

NEW QUESTION 26
What are the key considerations when selecting a storage solution for an AI data center that requires both high performance and scalability?

Only consider capacity; performance can be optimized later.

Focus solely on the lowest possible cost per terabyte.

Consider the balance of performance (IOPS, throughput), capacity, scalability, and cost.

Always choose the newest technology, regardless of cost or suitability.

Ignore the long-term data growth projections.

NEW QUESTION 27
You are deploying a DOCA application that needs to interact with the host operating system for certain tasks. What are the potential challenges and solutions for achieving this interaction securely and efficiently?

Challenges: Limited direct access to host resources, security concerns, and potential performance overhead. Solutions: Using DOCA Comm Channel for control message exchange, utilizing shared memory for data transfer, and employing secure APIs for host interaction.

Challenges: Kernel module compatibility issues and potential conflicts with host drivers. Solutions: Using standard Linux APIs whenever possible, avoiding direct kernel module modifications, and testing thoroughly for compatibility.

Challenges: Difficulty in debugging and troubleshooting issues across the host-DPU boundary. Solutions: Using comprehensive logging and tracing mechanisms, implementing remote debugging tools, and establishing clear communication channels between the host and DPU components.

Challenges: Resource contention between the host and DPU applications. Solutions: Using proper resource allocation and prioritization mechanisms, such as cgroups and QOS policies, to prevent resource starvation.

Solutions: Direct Memory Access on non secured memory for performance

NEW QUESTION 28
You are using NVIDIA Data Center GPU Manager (DCGM) to monitor your GPU cluster. You want to configure DCGM to automatically alert you when the GPU temperature exceeds a critical threshold. Which DCGM feature is MOST appropriate for this task?

DCGM Profiler

DCGM Telemetry

DCGM Health Checks

DCGM Group Management

DCGM Policy Management

NEW QUESTION 29
You are deploying a VMI container on a cloud platform that supports both NVIDIA vGPU and passthrough GPU access. Your workload requires maximum GPU performance and is not shared with other users. Which GPU access method is generally recommended for this scenario?

NVIDIA vGPU

GPU Passthrough

Shared GPU using CUDA MPS (Multi-Process Service)

Software Emulation of the GPU

It doesn’t matter; both vGPU and Passthrough offer the same performance.

NEW QUESTION 30
An AI research team requires access to GPU resources for both training and inference tasks. You are responsible for configuring the NVIDIA A100 GPUs using MIG. The training task requires high memory bandwidth, while the inference tasks require low latency. How would you configure MIG to best satisfy both workloads simultaneously?

Create MIG instances with the same memory and compute allocation for both training and inference.

Create a large MIG instance for training and a small MIG instance for inference.

Create a MIG instance with high memory bandwidth for training and a MIG instance with optimized compute for low-latency inference.

Create a single large MIG instance and allocate it dynamically between training and inference.

Don’t use MIG at all, and schedule tasks based on priority.

NEW QUESTION 31
Which of the following Slurm configuration options are typically modified within the “slurm.conf’ file? (Select TWO)

JobSubmitPlugins

SchedulerParameters

squeue

sacctmgr

MailType

NEW QUESTION 32
An AI model deployed through Fleet Command exhibits a vulnerability. You must urgently patch all edge devices with the updated model.
What is the fastest and safest way to accomplish this, minimizing disruption to ongoing operations?

Individually SSH into each device and manually replace the model files.

Use Fleet Command to orchestrate an over-the-air (OTA) update of the model to all devices simultaneously, potentially causing temporary service interruption.

Employ a staged rollout strategy within Fleet Command, gradually updating subsets of devices while monitoring for any issues before proceeding to the entire fleet.

Immediately shut down all edge devices to prevent further exploitation and then update the model offline.

Inform users to manually download and install the patch to all edge devices.

NEW QUESTION 33
You have a Kubernetes cluster with several nodes equipped with NVIDIA GPUs. You want to ensure that pods requesting GPUs are only scheduled on nodes that have the appropriate NVIDIA drivers and the NVIDIA Container Toolkit installed. Which Kubernetes feature(s) can you leverage to achieve this?

Node affinity and tolerations.

Resource quotas and limit ranges.

Pod priority and preemption.

Network policies and ingress controllers.

Taints and tolerations.

NEW QUESTION 34
You are deploying a cloud VMI container on AWS using the NVIDIA GPU Cloud (NGC) AMI. You need to ensure that the container has access to a specific S3 bucket containing the training dat a. Which of the following is the MOST secure and recommended method to grant this access?

Hardcoding AWS credentials directly within the container’s application code.

Using an IAM role assigned to the EC2 instance running the VMI container.

Storing AWS credentials in environment variables within the Dockerfile used to build the container image.

Creating a dedicated IAM user and distributing its credentials via AWS Systems Manager Parameter Store. Retrieve credentials during container startup.

Using AWS Secrets Manager to store the credentials and retrieving them via the AWS CLI within the container.

NEW QUESTION 35
A system administrator needs to scale a Kubernetes Job to 4 replicas.
What command should be used?

kubectl scale job -r 4

kubectl stretch job –replicas=4

kubectl scale job –replicas=4

kubectl autoscale deployment job –min=1 –max=10

NEW QUESTION 36
You are using the Run.ai CLI to monitor the status of a specific job with ID ‘job-1 23’. The job is currently in a ‘Pending’ state. What command would you use to get detailed information about why the job is pending?

runai describe job job-123

runai get events job-123

runai logs job-123

runai debug job-123

kubectl describe pod job-123

NEW QUESTION 37
You are managing multiple edge AI deployments using NVIDIA Fleet Command. You need to ensure that each AI application running on the same GPU is isolated from others to prevent interference.
Which feature of Fleet Command should you use to achieve this?

Remote Console

Secure NFS support

Multi-Instance GPU (MIG) support

Over-the-air updates

NEW QUESTION 38
You are using CUDA-Aware MPI for a distributed deep learning training job. After implementing CUDA-Aware MPI, you observe no performance improvement compared to regular MPI. What is the MOST likely reason?

The network interconnect is too slow.

The NCCL version is outdated.

The data being transferred is too small to benefit from GPU direct memory access.

The CPU is the bottleneck in the data loading pipeline.

The batch size is too small.