NCA-AIIO Nca - AI Infrastructure and Operations Practice Questions
Prepare for NCA-AIIO with more than an answer.
- Exam fee
- $125 USD
- Level
- Associate
- Valid for
- 2 years
Domains covered on the exam 3
- Essential AI Knowledge33%
- AI Infrastructure34%
- AI Operations33%
- 1
True or False: In the context of AI, 'training' refers to the process of using a pre-existing model to make predictions on new, unseen data, while 'inference' is the process of creating the model from a dataset.
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Correct answer: B
The statement has the definitions reversed. 'Training' is the computationally intensive process of creating the model by learning patterns from a dataset. 'Inference' is the process of using the trained model to make predictions on new data, which is typically less computationally demanding but requires low latency.
- 2
An administrator is tasked with setting up a job scheduling system for a shared GPU cluster used for both long-running AI training jobs and short, interactive data analysis tasks. The goal is to prevent large training jobs from monopolizing all resources and to ensure that interactive jobs have a reasonable wait time. Which job scheduling concept is most relevant to achieving this balance?
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Correct answer: B
Using a combination of priority-based queuing and preemption is the most effective strategy. Interactive jobs can be assigned to a high-priority queue. Preemption allows the scheduler (like Slurm or Kubernetes) to pause or suspend a low-priority, long-running training job to free up resources for a high-priority interactive job to run. This ensures responsiveness for interactive tasks while still allowing large jobs to complete, achieving the desired balance. FIFO would cause short jobs to get stuck behind long ones.
- 3
A data analytics team is using the NVIDIA RAPIDS suite to accelerate its ETL and machine learning pipelines. Which of the following statements accurately describe the core benefit of using RAPIDS? (Select TWO)
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Correct answer: B, D
- 4
A cloud architect is designing a network topology for a new multi-rack AI cluster built with NVIDIA HGX H100 servers. To support large-scale distributed training, the architect must minimize latency and maximize bandwidth for the traffic between servers (east-west traffic). Which networking technology is the industry standard for this type of high-performance cluster interconnect?
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Correct answer: B
NVIDIA Mellanox InfiniBand is the preferred networking technology for high-performance AI clusters. It offers extremely high bandwidth and ultra-low latency, which is critical for the intense inter-node communication (e.g., All-Reduce operations) in distributed training. Technologies like RDMA (Remote Direct Memory Access) over InfiniBand allow GPUs in different servers to communicate directly, bypassing CPU overhead and further reducing latency, making it superior to standard Ethernet for this use case.
- 5
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
graph TD A[Artificial Intelligence] --> B[Machine Learning] B --> C[Deep Learning]Show answer details
Correct answer: C
The relationship is hierarchical. Artificial Intelligence (AI) is the broad, overarching field of creating intelligent machines. Machine Learning (ML) is a subset of AI that focuses on algorithms that learn from data. Deep Learning (DL) is a further subset of ML that uses deep neural networks (with many layers) to learn complex patterns, and it has been the driving force behind recent AI breakthroughs.
- 6
A data center operations team is deploying a new NVIDIA DGX H100 SuperPOD. During the planning phase, they are debating cooling solutions. The primary goal is to maximize performance density while maintaining optimal operating temperatures under sustained, full-load training jobs. Which cooling technology is standard for the DGX H100 system to achieve this goal?
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Correct answer: C
The NVIDIA DGX H100 system utilizes a hybrid cooling approach with direct-to-chip liquid cooling for the highest heat-generating components like GPUs and CPUs, supplemented by air cooling. This design is crucial for dissipating the high thermal density of the system, allowing the GPUs to maintain peak performance under heavy AI workloads without thermal throttling. Standard forced-air cooling is insufficient for this thermal load, while immersion cooling is a more specialized data center-level solution, not an integrated feature of the DGX system itself.
- 7
An MLOps engineer is using the NVIDIA Data Center GPU Manager (DCGM) to monitor a cluster of GPUs running various training workloads. They notice that several GPUs are consistently reporting high
DCGM_FI_DEV_FB_USEDvalues, approaching 95% of capacity. What is the most direct operational concern indicated by this specific metric?Show answer details
Correct answer: C
The DCGM field identifier
DCGM_FI_DEV_FB_USEDspecifically tracks the used frame buffer (on-board GPU memory). A high value indicates that the models and data batches being processed are consuming almost all of the available VRAM. This can lead to out-of-memory (OOM) errors, forcing jobs to fail. It doesn't directly indicate compute utilization (which would beDCGM_FI_DEV_GPU_UTIL) or temperature (DCGM_FI_DEV_GPU_TEMP). - 8
A research institution wants to provide isolated GPU resources to multiple research teams from a single NVIDIA A100 server. Each team has small-to-medium sized workloads and does not require a full GPU. The primary requirement is hardware-level partitioning to ensure performance isolation and security between the teams' environments. Which NVIDIA technology should the administrator configure?
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Correct answer: B
Multi-Instance GPU (MIG) is the correct technology for this scenario. It is available on Ampere and newer architecture GPUs (like the A100 and H100) and allows a single physical GPU to be partitioned into up to seven secure, hardware-isolated GPU instances. Each instance has its own dedicated compute, memory, and cache resources, providing predictable performance and security, which perfectly matches the requirements. MPS allows multiple processes to share a single GPU context but does not provide the same level of hardware isolation.
- 9
True or False: NVIDIA's GPUDirect Storage technology allows data to be transferred directly between local or remote NVMe storage and GPU memory, bypassing the CPU and system memory entirely.
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Correct answer: A
This statement is true. GPUDirect Storage is a feature of the NVIDIA Magnum IO architecture that creates a direct data path between storage (like NVMe drives) and GPU memory. This path avoids the 'bounce buffer' in the CPU's system memory, which significantly reduces I/O latency, lowers CPU overhead, and increases data transfer bandwidth, accelerating workloads that are I/O bound.
- 10
A DevOps team is containerizing a legacy machine learning application that uses an older version of the CUDA toolkit. They are deploying this to a modern Kubernetes cluster managed by the NVIDIA GPU Operator. The new nodes have the latest NVIDIA drivers installed. Which component is responsible for ensuring that the application inside the container can communicate correctly with the host's driver, despite the potential version mismatch?
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Correct answer: B
The NVIDIA Container Toolkit is the core component that enables GPU support within containers. It works by mounting the necessary user-mode components of the host driver and device files into the container at runtime. This architecture decouples the CUDA toolkit version inside the container from the NVIDIA driver version on the host, as long as the host driver is new enough to support the hardware. This allows applications with older CUDA dependencies to run on modern infrastructure without modification.
