NVIDIA NCA-AIIO Practice Exams
Last updated on Oct 23,2026- Exam Code: NCA-AIIO
- Exam Name: AI Infrastructure and Operations
- Certification Provider: NVIDIA
- Latest update: Oct 23,2026
Your organization runs multiple AI workloads on a shared NVIDIA GPU cluster. Some workloads are more critical than others. Recently, you’ve noticed that less critical workloads are consuming more GPU resources, affecting the performance of critical workloads.
What is the best approach to ensure that critical workloads have priority access to GPU resources?
- A . Implement Model Optimization Techniques
- B . Upgrade the GPUs in the Cluster to More Powerful Models
- C . Use CPU-based Inference for Less Critical Workloads
- D . Implement GPU Quotas with Kubernetes Resource Management
Which of the following statements best differentiates AI, machine learning, and deep learning?
- A . AI is the broad concept of machines being able to perform tasks that require human intelligence, while machine learning is a subset of AI that involves learning from data, and deep learning is a further subset that uses neural networks
- B . Machine learning is a type of AI that specifically uses deep learning algorithms to make predictions
- C . Deep learning and AI are the same, and machine learning is a subset of deep learning
- D . Machine learning is synonymous with AI, and deep learning is just an alternative term for neural
networks
You are managing a high-performance computing cluster dedicated to training large-scale AI models. Recently, the performance has degraded significantly during training jobs, and the power consumption of the GPUs has spiked. You suspect it might be related to thermal throttling due to insufficient cooling.
Which of the following actions should you take to diagnose and address the issue? (Select two)
- A . Check the airflow and cooling system in the data center.
- B . Disable thermal throttling in the GPU settings.
- C . Increase the clock speed of the GPUs to improve performance.
- D . Monitor GPU temperature using tools like NVIDIA-SMI.
- E . Reduce the batch size of training jobs to decrease GPU load.
When virtualizing a GPU-accelerated infrastructure to support AI operations, what is a key factor to ensure efficient and scalable performance across virtual machines (VMs)?
- A . Increase the CPU allocation to each VM.
- B . Ensure that GPU memory is not overcommitted among VMs.
- C . Enable nested virtualization on the VMs.
- D . Allocate more network bandwidth to the host machine.
Your AI team notices that the training jobs on your NVIDIA GPU cluster are taking longer than expected. Upon investigation, you suspect underutilization of the GPUs.
Which monitoring metric is the most critical to determine if the GPUs are being underutilized?
- A . Memory Bandwidth Utilization
- B . GPU Utilization Percentage
- C . CPU Utilization
- D . Network Latency
You are tasked with deploying a real-time recommendation system for an e-commerce platform using NVIDIA AI infrastructure. The system needs to process millions of user interactions per second to provide personalized recommendations instantly.
Which NVIDIA solution is best suited to handle this workload efficiently?
- A . NVIDIA Clara
- B . NVIDIA TensorRT
- C . NVIDIA DGX Station
- D . NVIDIA Triton Inference Server
Your team is running an AI inference workload on a Kubernetes cluster with multiple NVIDIA GPUs. You observe that some nodes with GPUs are underutilized, while others are overloaded, leading to inconsistent inference performance across the cluster.
Which strategy would most effectively balance the GPU workload across the Kubernetes cluster?
- A . Deploying a GPU-aware scheduler in Kubernetes.
- B . Reducing the number of GPU nodes in the cluster.
- C . Implementing GPU resource quotas to limit GPU usage per pod.
- D . Using CPU-based autoscaling to balance the workload.
You are responsible for managing an AI infrastructure that includes multiple GPU clusters for deep learning workloads. One of your tasks is to efficiently allocate resources and manage workloads across these clusters using an orchestration platform.
Which of the following approaches would best optimize the utilization of GPU resources while ensuring high availability of the AI workloads?
- A . Implement a load-balancing algorithm that dynamically assigns workloads based on real-time GPU usage and workload requirements.
- B . Use a first-come, first-served (FCFS) scheduling policy across all clusters.
- C . Use a round-robin scheduling algorithm across all GPU clusters.
- D . Assign workloads to clusters based on a predefined static schedule.
A company is implementing a new AI-based recommendation system for its e-commerce platform, which handles millions of users and transactions daily. The company plans to deploy this system on an NVIDIA DGX platform to achieve the required performance. During initial testing, the AI model performs well on smaller datasets but struggles to handle the full-scale production data, leading to increased latency and degraded user experience.
Which of the following would be the most effective approach to resolve this issue?
- A . Scale the number of GPUs on the DGX platform to increase computational power.
- B . Replace the DGX platform with a traditional CPU-based server to handle the larger data volume.
- C . Decrease the batch size during inference to reduce memory usage.
- D . Use a smaller, simpler AI model to reduce computational requirements.
You are working on deploying a deep learning model that requires significant GPU resources across multiple nodes. You need to ensure that the model training is scalable, with efficient data transfer between the nodes to minimize latency.
Which of the following networking technologies is most suitable for this scenario?
- A . Fiber Channel
- B . Ethernet (1 Gbps)
- C . InfiniBand
- D . Wi-Fi 6