Ultimate Guide to Prepare Free NVIDIA NCP-AIO Exam Questions & Answer [Q24-Q47]

Q24. 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

Q25. 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.

Q26. Your Kubernetes cluster hosts several AI workloads with varying GPU requirements. Some workloads require high compute performance, while others are memory-bound. You want to optimize GPU resource allocation by bin-packing workloads with complementary resource profiles onto the same nodes. How would you approach this?

Use node affinity rules to schedule all workloads onto the same set of nodes, regardless of their resource profiles.

Employ a custom Kubernetes scheduler that considers GPU compute and memory usage when placing pods.

Manually assign workloads to specific nodes based on their resource requirements.

Configure resource quotas to limit the overall GPU usage in the cluster.

Implement a vertical pod autoscaler (VPA) to dynamically adjust the GPU resource requests of pods based on their actual usage.

Q27. You are deploying a cloud VMI container with Kubernetes. Your application requires a specific NVIDIA driver version. How do you ensure the correct driver version is used within the container, especially when the host node might have a different driver version?

Specify the desired driver version in the Kubernetes Deployment manifest using the ‘nvidia.com/gpu.driver.version’ resource limit.

Utilize the NVIDIA Device Plugin for Kubernetes and configure it to inject the correct driver libraries into the container during runtime.

Bake the required NVIDIA driver version directly into the container image during the Docker build process.

Configure the Kubernetes node’s operating system to always use the desired NVIDIA driver version globally.

The NVIDIA driver version used on the host node is automatically inherited by the container; no specific configuration is needed.

Q28. 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

Q29. Which command line utility can be used to verify the proper functioning of GPUDirect RDMA between two GPUs on different nodes?

nvidia-smi’

‘ibv devinfo’

rocminfo’

‘cuda-memcheck’

Ispci’

Q30. You are tasked with deploying a TensorFlow container from NGC on a Kubernetes cluster. The container requires specific NVIDIA drivers and libraries. Which of the following steps are essential to ensure successful deployment and GPU utilization?

Ensure the NVIDIA Container Toolkit is installed and configured on all worker nodes.

Deploy the container without specifying any resource limits or requests to allow it to utilize all available GPUs.

Verify that the NVIDIA drivers on the host machines match the versions required by the container.

Create a Kubernetes DaemonSet to automatically deploy and manage the NVIDIA device plugin on all nodes.

Bypass the NVIDIA Container Toolkit and directly use Docker to deploy the container.

Q31. You need to configure network settings for your Fleet Command deployment. You want to ensure that edge devices can only communicate with the Fleet Command server over a specific port and protocol for security reasons. Which of the following configurations is the MOST appropriate?

Open all ports on the edge devices and the Fleet Command server to allow unrestricted communication.

Configure a firewall on the edge devices and the Fleet Command server to allow communication only on the designated port and protocol (e.g., HTTPS on port 443),

Disable all network access on the edge devices except for SSH.

Rely on the default network settings provided by the operating system.

Configure a VPN for all communication, even local communication.

Q32. You are deploying BCM on a Kubernetes cluster that utilizes a custom ingress controller What configuration changes might be necessary to ensure external access to the BCM web interface?

Configure the ingress controller to forward traffic to the BCM service on the appropriate port (typically 3000).

Modify the BCM service type to ‘LoadBalancer’.

Create a custom resource definition (CRD) for BCM in the ingress controller.

Disable the default ingress controller and use the custom ingress controller exclusively.

Update the BCM Helm chart to automatically configure the ingress controller.

Q33. You have a DOCA application deployed on a BlueField-3 DPU. The application utilizes multiple DOCA services, including DOCA Flow and DOCA DPI. You are experiencing performance issues, and you suspect that the bottleneck is within the DPU. How would you proceed with debugging and profiling the DOCA application to identify the source of the performance bottleneck?

Using DOCA Telemetry: Enable DOCA Telemetry to collect performance metrics for different DOCA services and identify the service with the highest latency or resource consumption.

Using perf: Utilize the ‘perf tool on the DPU to profile the DOCA application and identify the functions or code sections that are consuming the most CPU cycles.

Using gdb: Attach ‘gdb’ to the DOCA application process and set breakpoints at strategic locations to analyze the application’s behavior and identify performance bottlenecks.

Using DOCA Tracing: Implement DOCA Tracing within the application code to record detailed information about function calls, memory allocations, and other events, then analyze the trace data to identify performance issues.

Analyzing DOCA logs: By analyzing DOCA Logs with debug mode, you can troubleshoot and profiling the DOCA application to identify the source of the performance bottleneck.

Q34. When deploying BCM using Helm, which Helm chart parameter controls the resource limits for the BCM pods (CPU and Memory)?

bcm.resources.limits’

‘resources.bcmLimits’

resources.limits’

‘bcmResources.limits’

‘deployment.resources.limits’

Q35. Your organization is running multiple AI models on a single A100 GPU using MIG in a multi-tenant environment. One of the tenants reports a performance issue, but you notice that other tenants are unaffected.
What feature of MIG ensures that one tenant’s workload does not impact others?

Hardware-level isolation of memory, cache, and compute resources for each instance.

Dynamic resource allocation based on workload demand.

Shared memory access across all instances.

Automatic scaling of instances based on workload size.

Q36. What is the main purpose of using Multi-lnstance GPU (MIG) with NVIDIA GPUs in a Kubernetes cluster managed by BCM, and what challenges does it help to address?

To improve the overall performance of GPU-accelerated applications by allowing multiple containers to share a single GPU’s memory and compute resources concurrently.

To enable the execution of CUDA-unaware applications within Kubernetes by abstracting away the underlying GPU hardware.

To increase GPU utilization and enable resource sharing in multi-tenant environments by partitioning a physical GPU into multiple isolated instances.

To simplify the deployment and management of GPU drivers and NVIDIA Container Toolkit on Kubernetes nodes.

To facilitate running CPU-bound workloads directly on GPUs to maximize resource utilization.

Q37. Consider an HPC application heavily reliant on CODA. You plan to leverage MIG to optimize GPU resource allocation within your cluster.
Which configuration approach would BEST ensure the HPC application benefits from high GPU compute capability while coexisting with other workloads?

Configure all MIG instances with equal memory and compute allocation to provide a fair distribution of resources.

Create a single, large MIG instance dedicated solely to the HPC application, maximizing its compute capacity.

Create MIG instances tailored to the HPC application’s specific memory and compute needs, allocating the necessary resources without over-provisioning. Utilize the remaining resources for other workloads.

Disable MIG and allow the HPC application to utilize the entire GPU for maximum performance.

Create multiple small MIG instances and distribute the HPC workload across them.

Q38. What must be done before installing new versions of DOCA drivers on a BlueField DPU?

Uninstall any previous versions of DOCA drivers.

Re-flash the firmware every time.

Disable network interfaces during installation.

Reboot the host system.

Q39. You’re running a Docker container with a deep learning model. While the model trains successfully, you observe that the GPU utilization fluctuates significantly, and the training process is slower than expected. What could be the cause and how would you address it?

The data loading pipeline is a bottleneck. Optimize data loading by using asynchronous data loaders and prefetching data to the GPU.

The batch size is too small. Increase the batch size to improve GPU utilization, but be mindful of GPU memory limitations.

The model is not large enough to saturate the GPU. Consider using a larger model or increasing the complexity of the computations.

There is CPU contention. Use ‘taskset to bind the data loading process to specific CPU cores to reduce CPU switching overhead.

The Docker container is not receiving enough CPU resources. Increase the CPU shares or CPU quota allocated to the container.

Q40. A data scientist reports that a Run.ai job is consistently crashing with a ‘SIGKILL’ signal. After verifying that the job is not exceeding its resource limits (CPU, memory, GPU), what is the MOST likely reason for this signal, and how can you diagnose it further within the Run.ai environment?

The job is experiencing a kernel panic on the node. Check the system logs on the node for kernel-related errors.

The Kubernetes liveness probe is failing, causing the pod to be restarted. Inspect the pod’s events and liveness probe configuration.

The Run.ai agent is terminating the job due to exceeding a pre-defined time limit. Check the job’s configuration for any time limits or deadlines.

The job is being preempted due to higher priority tasks, resulting in SIGKILL. Check the job’s priority, quota, and resource usage history with ACM.

The process is consuming a lot of disk I/O.

Q41. 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?

sinfo -R

scontrol show job <jobid>

sacct -j <job[.step]>

squeue -u <user_list>

Q42. Which of the following Slurm configuration options are typically modified within the “slurm.conf’ file? (Select TWO)

JobSubmitPlugins

SchedulerParameters

squeue

sacctmgr

MailType

Q43. What should an administrator check if GPU-to-GPU communication is slow in a distributed system using Magnum IO?

Limit the number of GPUs used in the system to reduce congestion.

Increase the system’s RAM capacity to improve communication speed.

Disable InfiniBand to reduce network complexity.

Verify the configuration of NCCL or NVSHMEM.

Q44. A Docker container that runs a PyTorch model is experiencing CUDA out-of-memory errors during training, even though ‘nvidia-smu reports that the GPU has sufficient free memory. You suspect memory fragmentation is the cause. How do you diagnose and mitigate this issue within the Docker environment?

Set the environment variable to force PyTorch’s memory allocator to be more aggressive in garbage collecting and splitting large memory blocks.

Use the function periodically during training to release unused GPU memory and defragment the memory pool.

Reduce the batch size and gradient accumulation steps to lower the overall memory footprint of the training process.

Use CUDA memory profiling tools like ‘NVIDIA Nsight Systems’ to identify specific memory allocations and deallocations causing fragmentation.

Restart the Docker container frequently during training to clear the memory and start with a fresh allocation state.

Q45. You are designing a data center that must support both interactive AI development and large-scale batch training jobs. You want to maximize GPU utilization while ensuring that interactive users have a responsive experience. Which of the following strategies is MOST effective?

Allocate dedicated GPUs to interactive users and run batch jobs on idle CPUs.

Use NVIDIA MPS (Multi-Process Service) to allow interactive users and batch jobs to share GPUs concurrently, and implement QOS to prioritize interactive workloads.

Schedule batch jobs to run only during off-peak hours when interactive users are not active.

Oversubscribe all GPUs to maximize utilization, even if it degrades interactive performance.

Run all workloads on a single large GPU server to simplify management.

Q46. 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.

Q47. You are tasked with optimizing the performance of a distributed deep learning training job running on multiple nodes interconnected with InfiniBand. You suspect that network communication is a bottleneck. Which tools and techniques would be MOST effective for diagnosing the issue?

Use ‘ibstat’ to check the status of the InfiniBand interfaces and identify any link errors or congestion.

Employ network profiling tools like ‘mpiP’ or NVIDIA Nsight Systems to analyze MPI communication patterns and identify bottlenecks.

Monitor network bandwidth utilization with tools like ‘iperf3’ to measure the actual throughput between nodes.

Use ‘nvidia-smi’ to monitor GPU utilization on each node.

Check the CPU utilization on each node using ‘top’.

Ultimate Guide to Prepare Free NVIDIA NCP-AIO Exam Questions & Answer [Q24-Q47]

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