NVIDIA NCA-AIIO Exam Questions Instant Access

Question 1

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

AMachine Learning is a subset of AI, and AI is a subset of Deep Learning.

BAI and Deep Learning are the same, while Machine Learning is a separate concept.

CAI is a subset of Machine Learning, and Machine Learning is a subset of Deep Learning.

DDeep Learning is a subset of Machine Learning, and Machine Learning is a subset of AI.

Answer : D

Artificial Intelligence (AI) is the overarching field encompassing techniques to mimic human intelligence. Machine Learning (ML), a subset of AI, involves algorithms that learn from data. Deep Learning (DL), a specialized subset of ML, uses neural networks with many layers to tackle complex tasks. This hierarchical relationship---DL within ML, ML within AI---is the correct differentiation, unlike the reversed or conflated options.

(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on AI, ML, and DL Definitions)

Question 2

When monitoring a GPU-based workload, what is GPU utilization?

AThe maximum amount of time a GPU will be used for a workload.

BThe GPU memory in use compared to available GPU memory.

CThe percentage of time the GPU is actively processing data.

DThe number of GPU cores available to the workload.

Answer : C

GPU utilization is defined as the percentage of time the GPU's compute engines are actively processing data, reflecting its workload intensity over a period (e.g., via nvidia-smi). It's distinct from memory usage (a separate metric), core counts, or maximum runtime, providing a direct measure of compute activity.

(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on GPU Monitoring)

Question 3

When deploying high-density workloads in a data center, what are the three main resource constraints that need to be considered?

AProcessing speed, storage capacity, and network connectivity.

BPower, cooling, and physical space.

CBandwidth, security, and redundancy.

Answer : B

High-density workloads (e.g., GPU clusters for AI) strain data center resources, primarily power (to supply dense servers), cooling (to dissipate heat from tightly packed hardware), and physical space (to house equipment). While processing speed, bandwidth, and other factors matter, power, cooling, and space are the physical constraints most critical to deployment feasibility.

(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Data Center Resource Constraints)

Question 4

When using an InfiniBand network for an AI infrastructure, which software component is necessary for the fabric to function?

AVerbs

BMPI

COpenSM

Answer : C

OpenSM (Open Subnet Manager) is essential for InfiniBand networks, managing the fabric by discovering topology, configuring switches and host channel adapters (HCAs), and handling routing. Without it, the fabric cannot operate. Verbs is an API for RDMA, and MPI is a communication protocol, but OpenSM is the critical software component for functionality.

(Reference: NVIDIA Networking Documentation, Section on InfiniBand Subnet Management)

Question 5

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

ANVLink

BGPUDirect P2P

CInfiniBand

DGPUDirect Storage

Answer : D

NVIDIA GPUDirect Storage enables direct data paths between GPU memory and local or remote storage (e.g., NVMe over fabrics), bypassing the CPU and host memory. This maximizes throughput and minimizes latency in AI data pipelines. NVLink connects GPUs, GPUDirect P2P facilitates GPU-to-GPU transfers, and InfiniBand is a network fabric, but only GPUDirect Storage targets storage access.

(Reference: NVIDIA GPUDirect Storage Documentation, Overview Section)

Question 6

For which workloads is NVIDIA Merlin typically used?

ARecommender systems

BNatural language processing

CData analytics

Answer : A

NVIDIA Merlin is a specialized, end-to-end framework engineered for building and deploying large-scale recommender systems. It streamlines the entire pipeline, including data preprocessing (e.g., feature engineering, data transformation), model training (using GPU-accelerated frameworks), and inference optimizations tailored for recommendation tasks. Unlike general-purpose tools for natural language processing or data analytics, Merlin is optimized to handle the unique challenges of recommendation workloads, such as processing massive user-item interaction datasets and delivering personalized results efficiently.

(Reference: NVIDIA Merlin Documentation, Overview Section)

Question 7

The foundation of the NVIDIA software stack is the DGX OS. Which of the following Linux distributions is DGX OS built upon?

AUbuntu

BRed Hat

CCentOS

Answer : A

DGX OS, the operating system powering NVIDIA DGX systems, is built on Ubuntu Linux, specifically the Long-Term Support (LTS) version. It integrates Ubuntu's robust base with NVIDIA-specific enhancements, including GPU drivers, tools, and optimizations tailored for AI and high-performance computing workloads. Neither Red Hat nor CentOS serves as the foundation for DGX OS, making Ubuntu the correct choice.

(Reference: NVIDIA DGX OS Documentation, System Requirements Section)

NVIDIA AI Infrastructure and Operations NCA-AIIO Exam Questions