NVIDIA NCP-AIO Exam Questions Instant Access

Question 1

Which of the following correctly identifies the key components of a Kubernetes cluster and their roles?

AThe control plane consists of the kube-apiserver, etcd, kube-scheduler, and kube-controller-manager, while worker nodes run kubelet and kube-proxy.

BWorker nodes manage the kube-apiserver and etcd, while the control plane handles all container runtimes.

CThe control plane is responsible for running all application containers, while worker nodes manage network traffic through etcd.

DThe control plane includes the kubelet and kube-proxy, and worker nodes are responsible for running etcd and the scheduler.

Answer : A

Comprehensive and Detailed Explanation From Exact Extract:

In Kubernetes architecture, the control plane is composed of several core components including the kube-apiserver, etcd (the cluster's key-value store), kube-scheduler, and kube-controller-manager. These manage the overall cluster state, scheduling, and orchestration of workloads. The worker nodes are responsible for running the actual containers and include the kubelet (agent that communicates with the control plane) and kube-proxy (handles network routing for services). Other options incorrectly assign these components or roles.

Question 2

A system administrator notices that jobs are failing intermittently on Base Command Manager due to incorrect GPU configurations in Slurm. The administrator needs to ensure that jobs utilize GPUs correctly.

How should they troubleshoot this issue?

AIncrease the number of GPUs requested in the job script to avoid using unconfigured GPUs.

BCheck if MIG (Multi-Instance GPU) mode has been enabled incorrectly and reconfigure Slurm accordingly.

CVerify that non-MIG GPUs are automatically configured in Slurm when detected, and adjust configurations if needed.

DEnsure that GPU resource limits have been correctly defined in Slurm's configuration file for each job type.

Answer : B

Comprehensive and Detailed Explanation From Exact Extract:

Misconfiguration related to MIG mode can cause Slurm to improperly allocate GPUs, leading to job failures. The administrator should verify whether MIG has been enabled on the GPUs and ensure that Slurm's configuration matches the hardware setup. If MIG is enabled, Slurm must be configured to recognize and schedule MIG partitions correctly to avoid resource conflicts.

Question 3

An administrator wants to check if the BlueMan service can access the DPU.

How can this be done?

AVia system logs

BVia the DOCA Telemetry Service (DTS)

CVia a lightweight database operating in the DPU server

DVia Linux dump files

Answer : B

Comprehensive and Detailed Explanation From Exact Extract:

The DOCA Telemetry Service (DTS) is used to monitor and verify the status and accessibility of services like BlueMan on NVIDIA DPUs. It provides telemetry data and health monitoring specific to the DPU and its services. System logs or dump files may provide indirect information but DTS is the targeted tool for this check.

Question 4

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?

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

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

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

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

Answer : B

Comprehensive and Detailed Explanation From Exact Extract:

To optimize scheduling of multi-GPU jobs in Slurm, it is essential to correctly specify GPU requests in job scripts using --gres=gpu:<number> and enable/configure Slurm's backfill scheduler. Backfill allows smaller jobs to run opportunistically in gaps without delaying larger multi-GPU jobs, improving cluster utilization and reducing wait times for multi-GPU jobs. Proper configuration ensures efficient packing and priority handling of GPU resources.

Question 5

Your organization is deploying an AI workload that requires high-throughput access to shared storage across multiple servers. The workload involves both training and inference tasks that need fast read and write speeds.

Which storage architecture would best support this AI workload?

AUse local storage on each server to minimize network traffic between nodes.

BPrioritize write performance over read performance since training tasks dominate AI workflows.

CA high-performance shared storage system that supports both high read and write IO performance.

DUse SSD-based shared storage systems to save costs while scaling up storage capacity.

Answer : C

Comprehensive and Detailed Explanation From Exact Extract:

For AI workloads involving both training and inference across multiple servers, a high-performance shared storage system that supports both high read and write I/O performance is essential. This ensures fast data access and efficient coordination between distributed compute nodes, preventing bottlenecks in data throughput. Local storage may minimize network traffic but lacks the necessary data sharing and coordination. Prioritizing only write performance neglects inference workload needs, and cost-saving SSD options might not deliver the required performance at scale. Hence, option C is the best choice for balanced, high-throughput AI workloads.

Question 6

A system administrator needs to configure and manage multiple installations of NVIDIA hardware ranging from single DGX BasePOD to SuperPOD.

Which software stack should be used?

ANetQ

BFleet Command

CMagnum IO

DBase Command Manager

Answer : D

Comprehensive and Detailed Explanation From Exact Extract:

NVIDIA's Base Command Manager is the software stack designed specifically for configuration, management, and monitoring of NVIDIA DGX systems, from a single DGX BasePOD up to large-scale SuperPOD deployments. It provides centralized management capabilities to orchestrate AI infrastructure, simplifying deployment, hardware monitoring, and lifecycle management across multiple clusters and data centers.

NetQ is focused on network monitoring and diagnostics rather than overall hardware cluster management.

Fleet Command is an enterprise SaaS solution to deploy and manage AI infrastructure in hybrid cloud environments but is not specifically targeted at on-premises DGX BasePOD to SuperPOD scale hardware management.

Magnum IO is NVIDIA's high-performance data and storage software stack for managing I/O but not hardware or cluster configuration management.

Therefore, Base Command Manager is the correct and dedicated tool for managing multiple installations of NVIDIA DGX hardware spanning from BasePOD to SuperPOD environments.

This is consistent with NVIDIA's official AI Operations documentation and product descriptions highlighting Base Command Manager as the unified command and control platform for AI infrastructure management.

Question 7

A Slurm user needs to submit a batch job script for execution tomorrow.

Which command should be used to complete this task?

Asbatch -begin=tomorrow

Bsubmit -begin=tomorrow

Csalloc -begin=tomorrow

Dsrun -begin=tomorrow

Answer : A

Comprehensive and Detailed Explanation From Exact Extract:

In Slurm cluster administration, the command to submit a batch job script is sbatch. This command schedules the job to be executed by the Slurm workload manager. The option -begin=tomorrow (or --begin=tomorrow) specifies the start time for the job execution, which in this case is set for tomorrow. The other commands have different purposes:

submit is not a valid Slurm command.

salloc is used to allocate resources interactively but does not submit batch jobs for scheduled execution.

srun runs jobs immediately on allocated resources but is typically used to launch tasks in an active job or interactively, not for batch job submission.

Therefore, the correct command to submit a batch job script for future execution is sbatch -begin=tomorrow.

NVIDIA AI Operations NCP-AIO Exam Questions