Latest NCP-AIO exam dumps with real NVIDIA questions and answers [Q28-Q47]

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Latest NCP-AIO exam dumps with real NVIDIA questions and answers

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NVIDIA NCP-AIO Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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 3	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 4	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.

QUESTION 28
A BCM pipeline is consistently crashing with a segmentation fault. How would you approach debugging this issue?

Use a debugger (e.g., gdb) to step through the code and identify the point of failure.

Examine the BCM pipeline logs for error messages or stack traces.

Run the BCM pipeline with a smaller dataset to isolate the problem.

Check for memory corruption issues using tools like Valgrind.

All of the above.

QUESTION 29
You’re managing a cluster using Kubernetes and Ceph, and your AI training jobs are experiencing storage I/O bottlenecks. You want to use Rook to manage Ceph within Kubernetes effectively. What configurations in Rook and Kubernetes would you verify to optimize storage performance for your AI workloads?

Ensure that the Ceph OSDs are running on fast storage devices (e.g., NVMe SSDs) and have sufficient resources (CPU, memory).

Verify that the Kubernetes pods have appropriate resource requests and limits to prevent resource contention.

Configure Ceph placement groups (PGs) and pools to match the workload characteristics (e.g., number of objects, access patterns).

Modify Rook’s default storage class to use the ‘rbd’ provisioner with optimized parameters for AI workloads (e.g., ‘imageFeatures: layering’

Disable Ceph monitoring within Rook to reduce overhead on the cluster. pool: data’).

QUESTION 30
You are configuring a storage system for storing the metadata associated with a large AI dataset. Metadata operations are I/O intensive but involve small files. Which storage solution is most appropriate for this scenario?

A high-capacity HDD array in a RAID 0 configuration

A high-performance NVMe SSD RAID array optimized for random read/write operations

A low-cost object storage service with high availability

A traditional file server with spinning disks

A tape library.

QUESTION 31
You have deployed the NVIDIA Device Plugin for Kubernetes on your BCM-managed cluster. After a kernel update on one of the worker nodes, the device plugin fails to discover the GPUs. The error messages indicate a mismatch between the driver version expected by the device plugin and the actual driver version installed on the node. What is the MOST reliable way to resolve this issue without disrupting other workloads?

Manually downgrade the NVIDIA driver on the affected worker node to match the version expected by the device plugin.

Update the NVIDIA Device Plugin deployment manifest to specify the driver version installed on the node.

Use a DaemonSet to manage the NVIDIA driver installation on all worker nodes, ensuring a consistent driver version across the cluster and compatibility with the device plugin.

Uninstall and reinstall the NVIDIA Container Toolkit on the affected worker node to automatically update the driver version.

Remove the NVIDIA Device Plugin and replace it with the ‘nvidia-driver-installer’ helm chart

QUESTION 32
You are troubleshooting a Run.ai job that is failing with a CUDA out-of-memory error, despite requesting a seemingly sufficient amount of GPU memory. What is the MOST likely cause of this issue?

The requested GPU count is too low.

The CUDA version on the node is incompatible with the application.

The job is using a larger batch size than the GPU memory can accommodate.

The job’s Docker image is corrupted.

The requested CPU count is too low.

QUESTION 33
You are troubleshooting a performance issue with a GPU-accelerated application running on Kubernetes managed by BCM. You suspect the application is not effectively utilizing the available GPU resources. Which of the following is the MOST effective way to gather detailed performance metrics and identify potential bottlenecks within the container?

Using ‘kubectl top pods’ to monitor the pod’s CPU and memory utilization.

Analyzing the application’s logs for error messages or performance warnings.

Using “nvidia-smi’ within the container to monitor GPU utilization, memory usage, and temperature.

Using ‘kubectl exec’ to run ‘top’ within the container and monitor process-level resource consumption.

Leveraging NVIDIA Nsight Systems or NVIDIA Nsight Compute to profile the application’s GPU kernel execution and identify performance bottlenecks.

QUESTION 34
You are trying to configure MIG (Multi-lnstance GPU) on your Run.ai cluster. You have an NVIDIAA100 GPU and want to create two MIG instances, each with 20GB of memory. Assuming the A100 has 80GB of memory, what is the CORRECT MIG profile string you would use when submitting a job to request one of these MIG instances?

1g.5gb

2g.20gb

1g.10gb

2g.10gb

4g.20gb

QUESTION 35
You have noticed that users can access all GPUs on a node even when they request only one GPU in their job script using –gres=gpu:1. This is causing resource contention and inefficient GPU usage.
What configuration change would you make to restrict users’ access to only their allocated GPUs?

Increase the memory allocation per job to limit access to other resources on the node.

Enable cgroup enforcement in cgroup.conf by setting ConstrainDevices=yes.

Set a higher priority for Jobs requesting fewer GPUs, so they finish faster and free up resources sooner.

Modify the job script to include additional resource requests for CPU cores alongside GPUs.

QUESTION 36
What steps should an administrator take if they encounter errors related to RDMA (Remote Direct Memory Access) when using Magnum IO?

Increase the number of network interfaces on each node to handle more traffic concurrently without using RDMA.

Disable RDMA entirely and rely on TCP/IP for all network communications between nodes.

Check that RDMA is properly enabled and configured on both storage and compute nodes for efficient data transfers.

Reboot all compute nodes after every job completion to reset RDMA settings automatically.

QUESTION 37
You are tasked with optimizing a BCM pipeline that processes video streams in real-time. The pipeline frequently misses frames, resulting in dropped video. What are the most effective strategies to reduce frame drops?

Reduce the resolution or frame rate of the input video streams.

Optimize the most computationally intensive stages of the pipeline using GPU acceleration (e.g., TensorRT).

Implement asynchronous processing to allow stages to run in parallel.

Increase the buffer size for video frames to accommodate processing delays.

All of the above.

QUESTION 38
Which BCM configuration file defines the operating system image used for provisioning new nodes?

cluster.yaml

node.yaml

image.yaml

provisioning.yaml

bcm.conf

QUESTION 39
You need to implement a highly available and fault-tolerant Fleet Command deployment for a mission-critical AI application. What architectural considerations are MOST important for ensuring resilience?

Deploy a single Fleet Command server in a standard configuration.

Utilize a multi-node Fleet Command cluster with redundancy and failover mechanisms, combined with geographically diverse edge device deployments.

Regularly back up the Fleet Command server’s configuration and data.

Monitor the Fleet Command server’s performance and resource utilization.

Rely solely on the edge devices’ ability to operate independently during Fleet Command outages.

QUESTION 40
A fleet of edge devices running AI inference applications experiences intermittent network connectivity. You need to configure Fleet Command to handle these disruptions gracefully. Which of the following actions should you take to ensure application resilience?

Configure Fleet Command to immediately roll back deployments when network connectivity is lost.

Implement a local caching mechanism on the edge devices to store inference results during network outages and synchronize them when connectivity is restored.

Disable all updates to the edge devices during periods of network instability.

Increase the timeout values for all Fleet Command operations.

Instruct users to manually restart applications on the edge devices after network outages.

QUESTION 41
You are deploying a cloud VMI container using Terraform. How would you define a resource to provision an NVIDIA GPU-enabled instance on AWS?

Terraform cannot be used to provision GPU-enabled instances.

Use packer instead of Terraform.

QUESTION 42
You’re using Docker Compose to manage a multi-container application that includes a GPU-accelerated container. The application runs fine locally, but when deployed to a cloud environment, the GPU container fails to start with a ‘device not found’ error. What are the potential reasons for this failure?

The cloud environment does not have NVIDIA GPUs available. Verify that the cloud instance type includes NVIDIA GPUs.

The NVIDIA drivers are not installed on the cloud instance. Install the appropriate NVIDIA drivers for the cloud instance’s operating system.

The Docker daemon on the cloud instance is not configured to use the NVIDIA runtime. Configure the Docker daemon as described in NVIDIA’s documentation.

The Docker Compose file does not specify the ‘-gpus all’ flag for the GPU container. Add ‘deploy: ‘ and ‘resources:’ sections to your docker-compose.yml to specify GPU requirements.

The Docker image is too large to be deployed in the cloud environment. Optimize the Docker image size to reduce deployment time.

QUESTION 43
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

QUESTION 44
A Docker container running a CUDA application terminates unexpectedly with an ‘out of memory’ error, despite the host machine having sufficient RAM. What are the potential causes and how would you diagnose them?

The container’s memory limit is too low. Increase the container’s memory limit using the -memory’ flag with ‘docker rum.

The GPU is running out of memory. Monitor GPU memory usage using ‘nvidia-smr within the container to identify memory leaks or excessive memory allocation.

The application is experiencing a memory leak within the container. Use memory profiling tools within the container to identify and fix the memory leak.

The host machine is experiencing swap thrashing. Monitor swap usage on the host machine and consider increasing RAM or optimizing the application’s memory usage.

The Docker image is corrupted. Rebuild the Docker image from scratch.

QUESTION 45
A data scientist submits a Run.ai job requesting 4 GPUs. However, due to resource constraints, only 2 GPUs are immediately available. You want the job to automatically start running as soon as the remaining 2 GPUs become available, without manual intervention. How do you configure Run.ai to achieve this?

Enable gang scheduling for the job.

Set the job’s ‘restartPolicy’ to ‘Always’.

Set a higher quota for the team.

Configure a lower priority for the job.

Use Run.ai’s ‘suspend’ and ‘resume’ commands manually.

QUESTION 46
A system administrator needs to scale a Kubernetes Job to 4 replicas.
What command should be used?

kubectl stretch job –replicas=4

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

kubectl scale job –replicas=4

kubectl scale job -r 4

QUESTION 47
You are deploying a DOCA application for network monitoring on a DPU. You need to capture and analyze specific network packets based on certain criteri a. Which DOCA service would be most suitable for this task, and how would you configure it?

DOCA Telemetry: Configure telemetry collectors to capture packet statistics and flow information, then analyze the collected data.

DOCA Flow: Define flow rules to match specific packets and trigger actions such as mirroring or redirection for further analysis.

DOCA DPI: Use deep packet inspection to analyze packet content and extract relevant information for monitoring purposes.

DOCA Comm Channel: Use Comm Channel for streaming packets from DPU to external monitoring entities.

DOCA Memory Domain (MD): Use shared memory between the host and the DPU to transfer captured packets for analysis on the host.

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NVIDIA NCP-AIO Exam Syllabus Topics:

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