NVIDIA NCA-AIIO Practice Exams
Last updated on Oct 22,2026- Exam Code: NCA-AIIO
- Exam Name: AI Infrastructure and Operations
- Certification Provider: NVIDIA
- Latest update: Oct 22,2026
Which of the following statements correctly highlights a key difference between GPU and CPU architectures?
- A . GPUs typically have higher clock speeds than CPUs, allowing them to process individual tasks faster.
- B . CPUs are optimized for parallel processing, making them better for AI workloads, while GPUs are designed for general-purpose tasks.
- C . CPUs are specialized for graphical computations, whereas GPUs handle general-purpose computing tasks.
- D . GPUs are optimized for parallel processing, with thousands of smaller cores, while CPUs have
fewer, more powerful cores optimized for sequential processing.
When virtualizing an infrastructure that includes GPUs to support AI workloads, what is one critical factor to consider to ensure optimal performance?
- A . Increase the number of virtual CPUs assigned to each VM.
- B . Disable hyper-threading on the host machine.
- C . Use GPU sharing technologies, like NVIDIA GRID, to allocate resources dynamically.
- D . Assign more storage to each virtual machine.
In your AI infrastructure, several GPUs have recently failed during intensive training sessions.
To proactively prevent such failures, which GPU metric should you monitor most closely?
- A . Power Consumption
- B . GPU Temperature
- C . GPU Driver Version
- D . Frame Buffer Utilization
You are optimizing an AI data center that uses NVIDIA GPUs for energy efficiency.
Which of the following practices would most effectively reduce energy consumption while maintaining performance?
- A . Disabling power capping to allow full power usage
- B . Enabling NVIDIA’s Adaptive Power Management features
- C . Utilizing older GPUs to reduce power consumption
- D . Running all GPUs at maximum clock speeds
Which of the following factors has most significantly contributed to the recent rapid improvements and widespread adoption of AI?
- A . The invention of new AI programming languages.
- B . The global standardization of AI ethics guidelines.
- C . Increased computational power, especially with the advent of modern GPUs and specialized AI hardware.
- D . The rise of social media, increasing the need for AI.
You are optimizing an AI-driven data center using NVIDIA DPUs to handle increasing network and storage demands. After implementing DPUs, you observe that some applications still suffer from high latency and bottlenecks.
What could be the most likely reason for this issue?
- A . The DPUs are configured to handle storage operations, but the network infrastructure is outdated.
- B . The DPUs are being used to offload AI inference tasks from the GPUs, causing inefficiencies.
- C . The DPUs are not configured to offload sufficient CPU tasks, causing CPU bottlenecks.
- D . The data center is using older-generation GPUs that are not fully compatible with the DPUs.
You are managing an AI cluster with critical deep learning workloads that must run without interruption. Some nodes in your Kubernetes cluster are occasionally experiencing failures, leading to job disruptions.
What is the most effective strategy to ensure high availability and minimize job disruptions in such a scenario?
- A . Use Single-node Deployment for Critical Jobs to Reduce Complexity
- B . Increase the Replica Count for All Jobs
- C . Disable Pod Preemption to Ensure Continuous Job Execution
- D . Configure Pod Anti-Affinity and Deploy Across Multiple Nodes
A company is deploying a large-scale AI training workload that requires distributed computing across multiple GPUs. They need to ensure efficient communication between GPUs on different nodes and optimize the training time.
Which of the following NVIDIA technologies should they use to achieve this?
- A . NVIDIA NVLink
- B . NVIDIA NCCL (NVIDIA Collective Communication Library)
- C . NVIDIA DeepStream SDK
- D . NVIDIA TensorRT
You are responsible for managing an AI data center that handles large-scale deep learning workloads. The performance of your training jobs has recently degraded, and you’ve noticed that the GPUs are underutilized while CPU usage remains high.
Which of the following actions would most likely resolve this issue?
- A . Reduce the batch size during training.
- B . Increase the GPU memory allocation.
- C . Optimize the data pipeline for better I/O throughput.
- D . Add more GPUs to the system.
You are managing an AI-driven autonomous vehicle project that requires real-time decision-making and rapid processing of large data volumes from sensors like LiDAR, cameras, and radar. The AI models must run on the vehicle’s onboard hardware to ensure low latency and high reliability.
Which NVIDIA solutions would be most appropriate to use in this scenario? (Select two)
- A . NVIDIA Tesla T4.
- B . NVIDIA DRIVE AGX Pegasus.
- C . NVIDIA Jetson AGX Xavier.
- D . NVIDIA GeForce RTX 3080.
- E . NVIDIA DGX A100.