Free NCA-AIIO Practice Questions: AI Infrastructure Fundamentals Domain

Free NCA-AIIO Practice Questions

Master the AI Infrastructure Fundamentals Domain

Part of Complete NCA-AIIO Study Series: This practice set covers the AI Infrastructure Fundamentals domain. For comprehensive exam preparation, start with our Complete NCA-AIIO Study Guide and explore all exam domains.

Test your knowledge in the AI Infrastructure Fundamentals domain with these 5 practice questions. Click an answer choice to instantly see the explanation and learn from each scenario.

Question 1

What is the main advantage of using NGC Containers for AI model deployment?

A) They are free to use on any cloud platform B) They provide pre-configured environments for AI frameworks C) They offer the fastest GPU performance D) They automatically scale with workload demands

Incorrect.
Licensing and cloud usage terms can vary; this isn’t the key benefit.

Correct – Answer B.
NGC Containers are pre-configured, optimized environments for popular AI frameworks, so teams can deploy and run models quickly without wrestling with drivers, libraries, and compatibility issues.

Incorrect.
They are optimized, but “fastest GPU performance” by itself is not the primary advantage.

Incorrect.
Autoscaling is handled by the surrounding orchestration platform, not by NGC containers alone.

Question 2

A company wants to deploy AI workloads in a cloud-native environment using containers. They require a solution that simplifies GPU management within Kubernetes. Which NVIDIA solution should they implement?

A) NVIDIA TensorRT B) NVIDIA NGC Catalog C) NVIDIA GPU Operator D) NVIDIA Base Command

Incorrect.
TensorRT focuses on inference optimization, not Kubernetes GPU lifecycle management.

Incorrect.
The NGC Catalog provides models and containers; it doesn’t automate GPU setup in Kubernetes.

Correct – Answer C.
NVIDIA GPU Operator automates installation and configuration of drivers, runtime, and related components to simplify GPU management in Kubernetes.

Incorrect.
Base Command is more about workflow and job management than cluster-level GPU orchestration.

Question 3

A company is running multiple AI training jobs on a single GPU and wants to ensure fair allocation of resources among these jobs. Which NVIDIA technology should they consider to achieve this?

A) NVIDIA Triton Inference Server B) NVIDIA GPU Operator C) Multi-Instance GPU (MIG) D) NVIDIA Base Command

Incorrect.
Triton handles inference serving, not low-level GPU partitioning for training jobs.

Incorrect.
GPU Operator manages drivers and stack components, but doesn’t itself partition GPUs.

Correct – Answer C.
MIG (Multi-Instance GPU) lets you slice a single GPU into multiple isolated instances, giving each job a defined share of resources and improving fairness and utilization.

Incorrect.
Base Command coordinates workflows; it doesn’t provide GPU partitioning like MIG.

Question 4

When designing a data pipeline for AI workloads, which of the following strategies best optimizes data throughput and minimizes latency?

A) Storing all data in cloud storage B) Using a high-speed local SSD array C) Implementing a complex ETL process D) Using a single-threaded data loader

Incorrect.
Cloud storage is flexible but often adds latency compared to local high-speed disks.

Correct – Answer B.
A high-speed local SSD array delivers low-latency, high-throughput access, keeping GPUs fed with data and reducing pipeline bottlenecks.

Incorrect.
Extra complexity in ETL can actually slow down throughput if not carefully designed.

Incorrect.
Single-threaded loaders underutilize modern CPUs and storage; multi-threaded/async loading is preferred.

Question 5

During a routine maintenance check of an AI infrastructure, you notice that one of the GPUs is consistently underutilized. What is a potential first step in troubleshooting this issue?

A) Increase the batch size of the AI workloads B) Check for driver updates and compatibility issues C) Reduce the number of active GPUs D) Disable all background processes

Incorrect.
Tuning batch size can help, but you should first rule out configuration and driver problems.

Correct – Answer B.
Verifying driver versions and compatibility is a logical first troubleshooting step, since outdated or mismatched drivers can cause poor GPU utilization.

Incorrect.
Reducing active GPUs can waste capacity and doesn’t explain why one GPU is underused.

Incorrect.
Background processes usually aren’t the primary cause of persistent underutilization.