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
In an effort to improve energy efficiency in your AI infrastructure using NVIDIA GPUs, you’re considering several strategies.
Which of the following would most effectively balance energy efficiency with maintaining performance?
- A . Disabling all energy-saving features to ensure maximum performance
- B . Employing NVIDIA GPU Boost technology to dynamically adjust clock speeds
- C . Running all GPUs at the lowest possible clock speeds
- D . Enabling deep sleep mode on all GPUs during processing times
A healthcare company is using NVIDIA AI infrastructure to develop a deep learning model that can
analyze medical images and detect anomalies. The team has noticed that the model performs well during training but fails to generalize when tested on new, unseen data.
Which of the following actions is most likely to improve the model’s generalization?
- A . Use a more complex neural network architecture
- B . Reduce the number of training epochs
- C . Apply data augmentation techniques
- D . Increase the batch size during training
You are managing an AI data center where multiple GPUs are orchestrated across a large cluster to run various deep learning tasks.
Which of the following actions best describes an efficient approach to cluster orchestration in this environment?
- A . Assign all jobs to the most powerful GPU in the cluster to maximize performance and minimize job duration.
- B . Implement a Kubernetes-based orchestration system to dynamically allocate GPU resources based on workload requirements and GPU availability.
- C . Use a round-robin scheduling algorithm to distribute jobs evenly across all GPUs, regardless of their individual workloads.
- D . Prioritize job assignments to GPUs with the least power consumption to reduce energy costs.
Your company is deploying a real-time AI-powered video analytics application across multiple retail stores. The application requires low-latency processing of video streams, efficient GPU utilization, and the ability to scale as more stores are added. The infrastructure will use NVIDIA GPUs, and the deployment must integrate seamlessly with existing edge and cloud infrastructure.
Which combination of NVIDIA technologies would best meet the requirements for this deployment?
- A . Deploy the application on NVIDIA DGX systems without utilizing edge devices.
- B . Use NVIDIA RAPIDS for video processing and store processed data in a local database.
- C . Leverage NVIDIA CUDA toolkit for development and deploy the application on generic cloud servers.
- D . Use NVIDIA Triton Inference Server on edge devices and NVIDIA NGC for model management.
Your team is tasked with analyzing a large dataset to extract meaningful insights that can be used to improve the performance of your AI models. The dataset contains millions of records from various sources, and you need to apply data mining techniques to uncover patterns and trends.
Which of the following data mining techniques would be most effective for discovering patterns in large datasets used in AI workloads? (Select two)
- A . Overfitting the model to ensure it captures all possible patterns.
- B . Using a flat file to store the entire dataset.
- C . K-means clustering to group similar data points.
- D . Principal Component Analysis (PCA) to reduce the dimensionality of the dataset.
- E . Applying dropout to prevent the model from memorizing patterns.
Which of the following statements best describes the difference between infrastructure requirements for training and inference in AI architectures?
- A . Inference requires distributed computing clusters, while training can be done on a single server
- B . Training requires more computational resources and memory bandwidth compared to inference
- C . Training typically runs on edge devices, while inference is run in centralized data centers
- D . Inference always requires more GPUs than training
When deploying AI workloads on a cloud platform using NVIDIA GPUs, which of the following is the most critical consideration to ensure cost efficiency without compromising performance?
- A . Selecting the instance with the maximum GPU memory available
- B . Using spot instances where applicable for non-critical workloads
- C . Running all workloads on a single, high-performance GPU instance to minimize costs
- D . Choosing a cloud provider that offers the lowest per-hour GPU cost
You are planning to deploy a large-scale AI training job in the cloud using NVIDIA GPUs.
Which of the following factors is most crucial to optimize both cost and performance for your deployment?
- A . Using reserved instances instead of on-demand instances
- B . Selecting instances with the highest available GPU core count
- C . Ensuring data locality by choosing cloud regions closest to your data sources
- D . Enabling autoscaling to dynamically allocate resources based on workload demand
Your AI team is using Kubernetes to orchestrate a cluster of NVIDIA GPUs for deep learning training jobs. Occasionally, some high-priority jobs experience delays because lower-priority jobs are consuming GPU resources.
Which of the following actions would most effectively ensure that high-priority jobs are allocated GPU resources first?
- A . Increase the Number of GPUs in the Cluster
- B . Configure Kubernetes Pod Priority and Preemption
- C . Manually Assign GPUs to High-Priority Jobs
- D . Use Kubernetes Node Affinity to Bind Jobs to Specific Nodes
You are managing an AI cluster where multiple jobs with varying resource demands are scheduled. Some jobs require exclusive GPU access, while others can share GPUs.
Which of the following job scheduling strategies would best optimize GPU resource utilization across the cluster?
- A . Increase the Default Pod Resource Requests in Kubernetes
- B . Schedule All Jobs with Dedicated GPU Resources
- C . Use FIFO (First In, First Out) Scheduling
- D . Enable GPU Sharing and Use NVIDIA GPU Operator with Kubernetes