NVIDIA NCP-AIO Exam Questions - Real Practice Questions for Guaranteed Success

Question 1

Your Kubernetes cluster is running a mixture of AI training and inference workloads. You want to ensure that inference services have higher priority over training jobs during peak resource usage times.

How would you configure Kubernetes to prioritize inference workloads?

AIncrease the number of replicas for inference services so they always have more resources than training jobs.

BSet up a separate namespace for inference services and limit resource usage in other namespaces.

CUse Horizontal Pod Autoscaling (HPA) based on memory usage to scale up inference services during peak times.

DImplement ResourceQuotas and PriorityClasses to assign higher priority and resource guarantees to inference workloads over training jobs.

Correct : D

Comprehensive and Detailed Explanation From Exact Extract:

To prioritize inference workloads over training jobs in Kubernetes, administrators should configure PriorityClasses and ResourceQuotas. PriorityClasses allow assigning different priority levels to pods, ensuring that during resource contention, higher-priority pods (inference services) receive resources first. ResourceQuotas limit the resource consumption per namespace or user, controlling overall usage and reserving capacity for critical workloads. This setup effectively manages resource allocation and guarantees performance for inference jobs during peak times.

Increasing replicas or namespaces alone does not guarantee priority during contention.

HPA scales based on metrics but does not manage priority or resource guarantees directly.

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AIncrease the number of replicas for inference services so they always have more resources than training jobs.

BSet up a separate namespace for inference services and limit resource usage in other namespaces.

CUse Horizontal Pod Autoscaling (HPA) based on memory usage to scale up inference services during peak times.

DImplement ResourceQuotas and PriorityClasses to assign higher priority and resource guarantees to inference workloads over training jobs.

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Question 2

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?

AReducing stripe count for large files would decrease parallelism, likely worsening performance for large sequential I/O operations.

BSeparate metadata-intensive operations and large sequential I/O operations by using different storage pools for each type of workload.

CIncrease the number of Object Storage Targets (OSTs) to handle more metadata operations.

DDisable GPUDirect Storage (GDS) during peak hours to reduce I/O load on the Lustre file system.

Correct : B

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.

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AReducing stripe count for large files would decrease parallelism, likely worsening performance for large sequential I/O operations.

BSeparate metadata-intensive operations and large sequential I/O operations by using different storage pools for each type of workload.

CIncrease the number of Object Storage Targets (OSTs) to handle more metadata operations.

DDisable GPUDirect Storage (GDS) during peak hours to reduce I/O load on the Lustre file system.

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Question 3

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=-o output.txt

B=-e output.txt

C=-output-output output.txt

Correct : A

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.

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A=-o output.txt

B=-e output.txt

C=-output-output output.txt

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Question 4

An administrator is troubleshooting issues with NVIDIA GPUDirect storage and must ensure optimal data transfer performance.

What step should be taken first?

AIncrease the GPU's core clock frequency.

BUpgrade the CPU to a higher clock speed.

CCheck for compatible RDMA-capable network hardware and configurations.

DInstall additional GPU memory (VRAM).

Correct : C

Comprehensive and Detailed Explanation From Exact Extract:

GPUDirect Storage performance relies heavily on RDMA-capable network hardware and proper configuration to enable direct memory access between storage and GPUs, bypassing CPU involvement for faster data transfers. Therefore, the first troubleshooting step should be to verify that RDMA-capable hardware is present and correctly configured. Adjusting GPU clocks, CPU speed, or GPU memory does not address the fundamental networking requirement for GPUDirect Storage.

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AIncrease the GPU's core clock frequency.

BUpgrade the CPU to a higher clock speed.

CCheck for compatible RDMA-capable network hardware and configurations.

DInstall additional GPU memory (VRAM).

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Question 5

You are deploying AI applications at the edge and want to ensure they continue running even if one of the servers at an edge location fails.

How can you configure NVIDIA Fleet Command to achieve this?

AUse Secure NFS support for data redundancy.

BSet up over-the-air updates to automatically restart failed applications.

CEnable high availability for edge clusters.

DConfigure Fleet Command's multi-instance GPU (MIG) to handle failover.

Correct : C

Comprehensive and Detailed Explanation From Exact Extract:

To ensure continued operation of AI applications at the edge despite server failures, NVIDIA Fleet Command allows administrators to enable high availability (HA) for edge clusters. This HA configuration ensures redundancy and failover capabilities, so applications remain operational when an edge server goes down.

Over-the-air updates handle software patching but do not inherently provide failover. MIG manages GPU resource partitioning, not failover. Secure NFS supports storage redundancy but is not the primary solution for application failover.

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AUse Secure NFS support for data redundancy.

BSet up over-the-air updates to automatically restart failed applications.

CEnable high availability for edge clusters.

DConfigure Fleet Command's multi-instance GPU (MIG) to handle failover.

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