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Free NVIDIA NCA-AIIO Practice Questions

29 free NVIDIA NCA-AIIO practice questions from the CertStash pack of 116, each with the correct answer and a full explanation. The rule is the same for every exam: ten free on the product page, 29 free here, all 116 in the pack, and each step includes the one before it.

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Question 1

A company is implementing a new network architecture and needs to consider the requirements and considerations for training and inference.

Which of the following statements is true about training and inference architecture?

  1. Training architecture and inference architecture have the same requirements and considerations.
  2. Training architecture is only concerned with hardware requirements, while inference architecture is only concerned with software requirements.
  3. Training architecture is focused on optimizing performance while inference architecture is focused on reducing latency.
  4. Training architecture and inference architecture cannot be the same.
Show answer and explanation

Correct answer: C. Training architecture is focused on optimizing performance while inference architecture is focused on reducing latency.

Training and inference have fundamentally different architectural priorities. Training architecture is optimized for maximum performance and throughput to process large datasets quickly, requiring high computational power and memory bandwidth. Inference architecture, conversely, is optimized for low latency and efficient resource utilization to serve predictions quickly with minimal computational overhead. This distinction shapes different hardware and software design choices for each phase.

Why the other options are wrong

  • A. Training and inference have distinctly different requirements; training needs high throughput while inference prioritizes low latency.
  • B. Both architectures involve both hardware and software considerations, not exclusive to one or the other.
  • D. Training and inference architectures can overlap or be the same in some deployments, though they often differ in optimization priorities.

Question 2

For which workloads is NVIDIA Merlin typically used?

  1. Recommender systems
  2. Natural language processing
  3. Data analytics
Show answer and explanation

Correct answer: A. Recommender systems

NVIDIA Merlin is specifically designed as a framework for building recommender systems. It provides tools and libraries optimized for recommendation engine workloads, including data preprocessing, feature engineering, and model training specifically tailored to recommendation scenarios.

Why the other options are wrong

  • B. While NLP is important to NVIDIA, Merlin is not the primary framework for natural language processing tasks.
  • C. General data analytics is not the specialized purpose of Merlin; it targets recommender systems specifically.

Question 3

Which NVIDIA parallel computing platform and programming model allows developers to program in popular languages and express parallelism through extensions?

  1. CUDA
  2. CUML
  3. CUGRAPH
Show answer and explanation

Correct answer: A. CUDA

CUDA (Compute Unified Device Architecture) is NVIDIA's parallel computing platform and programming model that allows developers to use popular languages like C, C++, and Python while expressing parallelism through language extensions and APIs. It abstracts the complexity of GPU programming while maintaining the performance benefits of parallel computation.

Why the other options are wrong

  • B. CUML is a machine learning library, not a parallel computing platform and programming model.
  • C. CUGRAPH is a graph processing library, not the fundamental parallel computing platform.

Question 4

Which of the following aspects have led to an increase in the adoption of AI? (Choose two.)

  1. Moore’s Law
  2. Rule-based machine learning
  3. High Powered GPUs
  4. Large amounts of data
Show answer and explanation

Correct answer: C, D. High Powered GPUs; Large amounts of data

The two primary factors driving increased AI adoption are high-powered GPUs, which provide the computational capability needed for training complex models, and large amounts of data, which supply the information required for models to learn meaningful patterns. Together, these enable practical deep learning applications at scale.

Why the other options are wrong

  • A. Moore's Law describes transistor density growth on chips and is not a primary driver of recent AI adoption acceleration.
  • B. Rule-based machine learning is an older paradigm and has not driven the recent surge in AI adoption; modern deep learning has superseded it.

Question 5

In training and inference architecture requirements, what is the main difference between training and inference?

  1. Training requires real-time processing, while inference requires large amounts of data.
  2. Training requires large amounts of data, while inference requires real-time processing.
  3. Training and inference both require large amounts of data.
  4. Training and inference both require real-time processing.
Show answer and explanation

Correct answer: B. Training requires large amounts of data, while inference requires real-time processing.

Training requires large amounts of data to learn patterns and optimize model parameters effectively. Inference, by contrast, takes an already-trained model and applies it to make predictions on new data, which demands real-time or near-real-time processing with minimal latency. This fundamental difference in requirements shapes the architecture and optimization strategies for each phase.

Why the other options are wrong

  • A. This reverses the correct relationship; training needs data volume, inference needs real-time speed.
  • C. Inference does not require large amounts of data; it works with individual samples or small batches.
  • D. Training does not require real-time processing; it can take hours or days to complete.

Question 6

Which of the following statements is true about GPUs and CPUs?

  1. GPUs are optimized for parallel tasks, while CPUs are optimized for serial tasks.
  2. GPUs have very low bandwidth main memory while CPUs have very high bandwidth main memory.
  3. GPUs and CPUs have the same number of cores, but GPUs have higher clock speeds.
  4. GPUs and CPUs have identical architectures and can be used interchangeably.
Show answer and explanation

Correct answer: A. GPUs are optimized for parallel tasks, while CPUs are optimized for serial tasks.

GPUs are designed with thousands of small cores optimized for parallel processing of similar operations across many data elements simultaneously. CPUs are designed with fewer cores optimized for sequential, serial task execution with high clock speeds and complex control logic. This architectural difference makes GPUs superior for data-parallel workloads while CPUs excel at single-threaded performance.

Why the other options are wrong

  • B. GPUs actually have very high bandwidth main memory compared to CPUs, not low; this is essential for parallel processing.
  • C. GPUs have far more cores than CPUs (thousands vs tens), though at lower individual clock speeds.
  • D. GPUs and CPUs have fundamentally different architectures and are not interchangeable for most workloads.

Question 7

Which two components are included in GPU Operator? (Choose two.)

  1. Drivers
  2. PYTorch
  3. DCGM
  4. TensorFlow
Show answer and explanation

Correct answer: A, C. Drivers; DCGM

The NVIDIA GPU Operator includes drivers (GPU drivers needed for hardware communication) and DCGM (Data Center GPU Manager, for monitoring and management). These components handle the low-level GPU infrastructure and system management necessary for GPU deployment in containerized environments.

Why the other options are wrong

  • B. PyTorch is a machine learning framework that may be used with GPU Operator but is not a component of it.
  • D. TensorFlow is a machine learning framework that may be used with GPU Operator but is not a component of it.

Question 8

Which phase of deep learning benefits the greatest from a multi-node architecture?

  1. Data Augmentation
  2. Training
  3. Inference
Show answer and explanation

Correct answer: B. Training

Training benefits most significantly from multi-node architecture because it involves processing large datasets and performing compute-intensive optimization across many training steps, which can be parallelized across multiple GPUs and nodes. This distributed training reduces overall training time substantially. Data augmentation is preprocessing and inference is using a trained model, neither of which benefits as greatly from multi-node scaling.

Why the other options are wrong

  • A. Data augmentation is a preprocessing step that doesn't benefit significantly from multi-node architectures.
  • C. Inference typically runs on a single or few nodes to serve predictions with minimal latency; it doesn't require multi-node scaling like training does.

Question 9

Which architecture is the core concept behind large language models?

  1. BERT Large model
  2. State space model
  3. Transformer model
  4. Attention model
Show answer and explanation

Correct answer: C. Transformer model

The Transformer model is the foundational architecture behind modern large language models. It introduced the self-attention mechanism that allows models to process sequences in parallel and capture long-range dependencies effectively. This architecture powers GPT, BERT, and other state-of-the-art LLMs. While BERT is a specific transformer-based model, attention is a component within transformers, and state space models are alternative architectures; the Transformer itself is the core concept.

Why the other options are wrong

  • A. BERT Large is a specific model implementation based on transformers, not the core architecture concept itself.
  • B. State space models are alternative neural architectures that are not the primary foundation for most modern LLMs.
  • D. Attention is a crucial mechanism within transformers but is not itself the core architecture, it's a component of it.

Question 10

What is a key value of using NVIDIA NIMs?

  1. They provide fast and simple deployment of AI models.
  2. They have community support.
  3. They allow the deployment of NVIDIA SDKs.
Show answer and explanation

Correct answer: A. They provide fast and simple deployment of AI models.

NVIDIA NIMs (Neural Information Microservices) are designed to provide fast and simple deployment of pre-trained AI models in containerized environments. This is their primary value proposition, they enable rapid productionization of models without requiring extensive infrastructure knowledge. While community support exists and they support NVIDIA tooling, the key differentiating value is the simplicity and speed of deployment.

Why the other options are wrong

  • B. Community support is present but is not the primary key value that distinguishes NIMs.
  • C. NIMs support NVIDIA tools and SDKs, but enabling SDK deployment is not their primary purpose; they abstract complexity for easier model deployment.

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Question 11

Which characteristic best describes inference when comparing inference to deep learning training?

  1. Inference requires smaller amounts of hardware.
  2. Inference has larger data requirements.
  3. Inference takes longer to evaluate.
Show answer and explanation

Correct answer: A. Inference requires smaller amounts of hardware.

Inference requires smaller amounts of hardware compared to training because it only needs to perform forward passes through an already-trained model. Training requires significant computational resources for backpropagation, gradient calculations, and processing massive datasets, making it far more hardware-intensive than inference.

Why the other options are wrong

  • B. Inference has smaller data requirements; training requires complete training datasets.
  • C. Inference is faster than training; a forward pass through a trained model is computationally simpler than iterative training.

Question 12

How many distinct network fabrics are in an AI cluster?

  1. 3
  2. 2
  3. 4
  4. 5
Show answer and explanation

Correct answer: C. 4

A typical enterprise AI cluster contains 4 distinct network fabrics: (1) management/out-of-band network, (2) high-speed GPU interconnect fabric (InfiniBand or Ethernet), (3) storage network, and (4) in-band Ethernet for general cluster communication. These separate fabrics serve different purposes and optimize traffic patterns for their respective functions.

Why the other options are wrong

  • A. 3 fabrics is insufficient to handle the diverse networking requirements of a modern AI cluster.
  • B. 2 fabrics cannot adequately separate management, compute, storage, and general communication traffic.
  • D. 5 fabrics exceeds the typical number of distinct network types deployed in standard AI cluster architectures.

Question 13

Which NVIDIA software provides the capability to virtualize a GPU?

  1. Horizon
  2. vGPU
  3. virtGPU
Show answer and explanation

Correct answer: B. vGPU

NVIDIA vGPU (virtual GPU) is NVIDIA's software solution that enables GPU virtualization, allowing multiple virtual machines to securely share a single physical GPU. This technology is used to maximize GPU utilization in virtualized data center environments.

Why the other options are wrong

  • A. Horizon is VMware's desktop virtualization platform, not an NVIDIA GPU virtualization tool.
  • C. virtGPU is not an established NVIDIA product name; vGPU is the correct NVIDIA virtualization solution.

Question 14

When training a neural network, what is the most common pattern of storage access?

  1. Random write
  2. Sequential read
  3. Sequential write
Show answer and explanation

Correct answer: B. Sequential read

During neural network training, data is typically loaded from storage in sequential batches for processing through the network. This sequential read pattern is the dominant access mode because training loops iterate through datasets sequentially, loading contiguous chunks of training data into memory for forward and backward passes. Random writes and sequential writes are not characteristic of the training inference pattern itself.

Why the other options are wrong

  • A. Random write is not a characteristic pattern during neural network training; the network reads data sequentially rather than writing randomly.
  • C. Sequential write occurs during checkpointing/saving, but the dominant storage access pattern during active training is reading data sequentially.

Question 15

Which are three key features of InfiniBand networking technology?

  1. High reliability, high latency, and CPU offloads.
  2. High latency, high reliability, and high bandwidth.
  3. GPU offloads, low latency, high reliability.
  4. Low latency, high bandwidth, and CPU offloads.
Show answer and explanation

Correct answer: D. Low latency, high bandwidth, and CPU offloads.

InfiniBand networking is characterized by low latency, high bandwidth, and CPU offloading capabilities through RDMA and other hardware acceleration features. These three features make it ideal for high-performance computing environments. Option A incorrectly lists high latency, option B also incorrectly includes high latency, and option C omits high bandwidth which is a critical feature.

Why the other options are wrong

  • A. InfiniBand features low latency, not high latency, and this option mixes latency direction with incorrect terminology.
  • B. InfiniBand provides low latency, not high latency; high latency would defeat the purpose of the technology.
  • C. While GPU offloads and low latency are correct, high bandwidth is equally critical and should be included instead of omitting it.

Question 16

Which feature of RDMA reduces CPU utilization and lowers latency?

  1. Increased memory buffer size.
  2. Network adapters that include hardware offloading.
  3. NVIDIA Magnum I/O software.
Show answer and explanation

Correct answer: B. Network adapters that include hardware offloading.

Network adapters with hardware offloading are the feature of RDMA that reduces CPU utilization and lowers latency by moving network protocol processing from the CPU to specialized network hardware. This allows data to bypass the kernel and CPU entirely. Option A relates to buffering capacity rather than CPU reduction, and option C is NVIDIA software that complements but doesn't fundamentally reduce RDMA latency.

Why the other options are wrong

  • A. Increased memory buffer size affects throughput and capacity, not the fundamental CPU utilization or latency reduction that RDMA provides.
  • C. NVIDIA Magnum I/O is software for optimizing I/O, not the core hardware feature that enables RDMA's CPU offloading benefits.

Question 17

What is the importance of a job scheduler in an AI resource-constrained cluster?

  1. It allocates resources based on which job requests came first.
  2. It ensures the all jobs in the cluster are executed simultaneously.
  3. It increases the number of resources available in the cluster.
  4. It allocates resources efficiently and optimizes job execution.
Show answer and explanation

Correct answer: D. It allocates resources efficiently and optimizes job execution.

A job scheduler in an AI cluster is critical for allocating limited resources efficiently across competing jobs and optimizing overall execution. It manages queue ordering, resource allocation, job prioritization, and scheduling policies to maximize cluster throughput and utilization while minimizing wait times and resource waste.

Why the other options are wrong

  • A. While FIFO is one scheduling policy, job schedulers do far more than just first-come-first-served allocation; they optimize resource usage.
  • B. Executing all jobs simultaneously is impossible in resource-constrained environments and defeats the purpose of a scheduler.
  • C. Job schedulers do not create new resources; they intelligently allocate existing resources more effectively.

Question 18

In a data center, what is the purpose and benefit of a DPU?

  1. A DPU is responsible for providing backup and disaster recovery solutions.
  2. A DPU is used for managing physical infrastructure, such as power and cooling.
  3. A DPU is responsible for managing network connections and security.
  4. A DPU is designed to offload, accelerate, and isolate infrastructure workloads.
Show answer and explanation

Correct answer: D. A DPU is designed to offload, accelerate, and isolate infrastructure workloads.

A DPU (Data Processing Unit) is designed to offload, accelerate, and isolate infrastructure workloads from the main CPU. DPUs handle networking, security, storage, and other infrastructure tasks, allowing the CPU to focus on application workloads. This separation improves security, reduces CPU overhead, and accelerates infrastructure-level operations.

Why the other options are wrong

  • A. Backup and disaster recovery are typically handled by dedicated software or appliances, not DPUs.
  • B. Physical infrastructure management (power, cooling) is handled by building management systems, not DPUs.
  • C. While DPUs do manage network connections and security, this is only part of their broader infrastructure offloading role.

Question 19

What is the primary command for checking the GPU utilization on a single DGX H100 system?

  1. nvidia-smi
  2. ctop
  3. nvml
Show answer and explanation

Correct answer: A. nvidia-smi

nvidia-smi (NVIDIA System Management Interface) is the standard command-line utility for monitoring GPU utilization, memory usage, temperature, and other metrics on NVIDIA GPUs including DGX H100 systems. It provides real-time visibility into GPU performance and is the primary tool used by system administrators and users for GPU health checks.

Why the other options are wrong

  • B. ctop is a container-level monitoring tool for viewing resource usage, not specifically for GPU monitoring.
  • C. NVML (NVIDIA Management Library) is a programming interface/library for accessing GPU information, not a command-line tool for direct monitoring.

Question 20

Which aspect of computing uses large amounts of data to train complex neural networks?

  1. Machine learning
  2. Deep learning
  3. Inferencing
Show answer and explanation

Correct answer: B. Deep learning

Deep learning specifically refers to the process of using large amounts of data to train complex neural networks with multiple layers. It is characterized by the use of deep neural network architectures to learn hierarchical representations from data. While machine learning is broader and includes non-neural approaches, and inferencing is the deployment phase, deep learning is the specific paradigm of training complex neural networks on large datasets.

Why the other options are wrong

  • A. Machine learning is broader and encompasses many techniques beyond neural networks.
  • C. Inferencing refers to the process of using a trained model for predictions, not the training process on large datasets.

Question 21

Which is the best PUE value for a data center?

  1. PUE of 1.2
  2. PUE of 3.5
  3. PUE of 5.0
  4. PUE of 2.0
Show answer and explanation

Correct answer: A. PUE of 1.2

PUE (Power Usage Effectiveness) is a metric where lower values indicate better energy efficiency. A PUE of 1.2 is the best value among the options, meaning only 20% overhead beyond the 100% consumed by IT equipment. This represents highly efficient cooling and power delivery systems. Values above 2.0 indicate increasingly wasteful infrastructure.

Why the other options are wrong

  • B. PUE of 3.5 indicates that 71% of energy is wasted on cooling and power delivery, which is poor efficiency.
  • C. PUE of 5.0 means 80% of energy is wasted on non-IT overhead, representing very poor data center efficiency.
  • D. PUE of 2.0 indicates 50% overhead, which is better than 3.5 or 5.0 but significantly worse than 1.2.

Question 22

Which of the following statements correctly differentiates between AI, Machine Learning, and Deep Learning?

  1. Machine Learning is a subset of AI, and AI is subset of Deep Learning
  2. AI and Deep Learning are the same, while Machine Learning is a separate concept.
  3. AI is a subset of Machine Learning, and Machine Learning is a subset of Deep Learning.
  4. Deep Learning is a subset of Machine Learning, and Machine Learning is a subset of AI.
Show answer and explanation

Correct answer: D. Deep Learning is a subset of Machine Learning, and Machine Learning is a subset of AI.

The correct hierarchical relationship is: Deep Learning is a subset of Machine Learning, and Machine Learning is a subset of AI. AI is the broadest field encompassing any technique that makes computers appear intelligent. Machine Learning is the subset of AI focused on learning from data. Deep Learning is the subset of Machine Learning that uses neural networks with multiple layers. This represents the proper conceptual hierarchy.

Why the other options are wrong

  • A. This reverses the relationship; AI is the broadest concept, not the smallest.
  • B. AI and Deep Learning are not equivalent; AI is much broader and includes many non-deep-learning approaches.
  • C. This inverts the correct hierarchy; AI should be the broadest concept, not the smallest.

Question 23

A customer is evaluating an AI cluster for training and is questioning why they should use a large number of nodes.

Why would multi-node training be advantageous?

  1. The model is too large to fit into GPU memory
  2. The model is being used by a large number of users
  3. The model is being used for large scale inference workloads
Show answer and explanation

Correct answer: A. The model is too large to fit into GPU memory

Multi-node training is advantageous when the model is too large to fit into a single GPU's memory, requiring distributed training across multiple nodes to parallelize both computation and memory usage. Option B relates to inference/serving scenarios rather than training, and option C also describes inference workloads. The primary driver for multi-node training in AI clusters is model size and computational complexity.

Why the other options are wrong

  • B. Large numbers of users accessing a model is an inference/serving concern, not a training architecture decision.
  • C. Large-scale inference workloads are handled through inference infrastructure, not multi-node training clusters.

Question 24

What is one key advantage that Cloud GPU Infrastructure has over On-Prem GPU infrastructure?

  1. Lower cost barrier to entry.
  2. Reduced cost of I/O traffic.
  3. Greater flexibility for hardware orchestration.
Show answer and explanation

Correct answer: A. Lower cost barrier to entry.

A key advantage of cloud GPU infrastructure is the lower cost barrier to entry, organizations can access powerful GPUs without massive upfront capital investments and can pay only for resources used. Option B is false (cloud I/O traffic typically incurs additional egress costs), and option C is not a particular cloud advantage over on-prem for hardware orchestration.

Why the other options are wrong

  • B. Cloud infrastructure typically charges for I/O and data transfer traffic, making it more costly than on-premises for data-intensive workloads.
  • C. On-premises infrastructure can offer equally flexible hardware orchestration; this is not a distinctive cloud advantage.

Question 25

What enables moving data between GPU memory and local or remote storage without using the CPU?

  1. NVLink
  2. GPUDirect P2P
  3. InfiniBand
  4. GPUDirect Storage
Show answer and explanation

Correct answer: D. GPUDirect Storage

GPUDirect Storage (GDS) enables direct data movement between GPU memory and NVMe/storage devices without CPU involvement. This bypasses the CPU memory hierarchy entirely, reducing latency and freeing CPU resources. While NVLink, GPUDirect P2P, and InfiniBand facilitate different GPU-to-GPU or GPU-to-network transfers, only GDS specifically handles the GPU-to-storage path directly.

Why the other options are wrong

  • A. NVLink provides direct GPU-to-GPU connections on the same system, not GPU-to-storage connectivity.
  • B. GPUDirect P2P handles peer-to-peer transfers between GPUs on the same system, not storage access.
  • C. InfiniBand is a network fabric for remote communication; GPUDirect Storage is what enables direct GPU-storage transfers.

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Question 26

Which architecture, training or inference, requires more data storage?

  1. Training and inference architecture require the same amount of data storage.
  2. Training architecture requires more data storage.
  3. Inference architecture requires more data storage.
Show answer and explanation

Correct answer: B. Training architecture requires more data storage.

Training architecture requires significantly more data storage because it must store entire datasets, intermediate model checkpoints, gradients, and accumulated statistics during the learning process. Inference only requires the trained model weights and typically processes single or small batches of data for predictions.

Why the other options are wrong

  • A. Training and inference have vastly different storage requirements.
  • C. Inference architecture requires minimal data storage compared to training, which needs complete training datasets.

Question 27

What is a key benefit of using NVIDIA GPUDirect RDMA in an AI environment?

  1. It increases the power efficiency and thermal management of GPUs.
  2. It reduces the latency and bandwidth overhead of remote memory access between GPUs.
  3. It enables faster data transfers between GPUs and CPUs without involving the operating system.
  4. It allows multiple GPUs to share the same memory space without any synchronization.
Show answer and explanation

Correct answer: B. It reduces the latency and bandwidth overhead of remote memory access between GPUs.

GPUDirect RDMA (Remote Direct Memory Access) enables one GPU to directly access the memory of another GPU across the network, bypassing the host CPU entirely. This reduces latency and bandwidth overhead because data doesn't need to traverse the CPU memory hierarchy and PCI-e paths, allowing for faster GPU-to-GPU communication over high-speed interconnects like InfiniBand.

Why the other options are wrong

  • A. GPUDirect RDMA does not directly improve power efficiency or thermal management; it focuses on communication performance.
  • C. That describes GPUDirect P2P, which handles GPU-CPU transfers; RDMA specifically enables remote GPU-to-GPU access.
  • D. GPUDirect RDMA does not enable shared memory without synchronization; it enables direct access with proper synchronization mechanisms.

Question 28

Which of the following statements is true about Kubernetes orchestration?

  1. It is bare-metal based but it supports containers.
  2. It has advanced scheduling capabilities to assign jobs to available resources.
  3. It has no inferencing capabilities.
  4. It does load balancing to distribute traffic across containers.
Show answer and explanation

Correct answer: B. It has advanced scheduling capabilities to assign jobs to available resources.

Kubernetes has advanced scheduling capabilities that automatically assign workloads (pods and jobs) to available cluster resources based on resource requirements, constraints, and policies. This is a core strength of Kubernetes. Kubernetes also supports load balancing for traffic distribution across containers, but its defining orchestration feature is this scheduling function.

Why the other options are wrong

  • A. Kubernetes is container-native orchestration software; while it can run on bare metal, it is not bare-metal based.
  • C. Kubernetes supports inferencing through deployed containers and has ecosystem tools for ML inference.
  • D. While Kubernetes does provide load balancing, the consensus answer prioritizes the scheduling capability.

Question 29

Which of the following NVIDIA tools is primarily used for monitoring and managing AI infrastructure in the enterprise?

  1. NVIDIA NeMo System Manager
  2. NVIDIA Data Center GPU Manager
  3. NVIDIA DGX Manager
  4. NVIDIA Base Command Manager
Show answer and explanation

Correct answer: B. NVIDIA Data Center GPU Manager

NVIDIA Data Center GPU Manager (DCGM) is the primary tool designed for monitoring, managing, and maintaining enterprise AI infrastructure at scale. It provides comprehensive monitoring, health checks, diagnostic capabilities, and management features specifically built for data center GPU deployments.

Why the other options are wrong

  • A. NVIDIA NeMo System Manager is related to NeMo framework for NLP tasks, not enterprise infrastructure management.
  • C. NVIDIA DGX Manager manages DGX systems but is more limited in scope compared to the broader enterprise data center focus of DCGM.
  • D. While Base Command Manager exists, DCGM is the established standard tool for data center GPU monitoring and management.

That was 29 of 116.

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