Practice Free NCA-AIIO Exam Online Questions
In an AI infrastructure setup, you need to optimize the network for high-performance data movement between storage systems and GPU compute nodes.
Which protocol would be most effective for achieving low latency and high bandwidth in this environment?
- A . TCP/IP
- B . Remote Direct Memory Access (RDMA)
- C . SMTP
- D . HTTP
You are working on a project that involves both real-time AI inference and data pre-processing tasks. The AI models require high throughput and low latency, while the data pre-processing involves complex logic and diverse data types. Given the need to balance these tasks, which computing architecture should you prioritize for each task?
- A . Use CPUs for both AI inference and data pre-processing.
- B . Use GPUs for both AI inference and data pre-processing.
- C . Prioritize GPUs for AI inference and CPUs for data pre-processing.
- D . Deploy AI inference on CPUs and data pre-processing on FPGAs.
A company is planning to virtualize its AI infrastructure to improve resource utilization and manageability. The operations team must ensure that the virtualized environment can effectively support GPU-accelerated workloads.
Which two key considerations should the team prioritize? (Select two)
- A . Prioritizing VM storage capacity over GPU allocation
- B . Utilizing NVIDIA vGPU technology for partitioning GPUs among multiple VMs
- C . Allocating more CPU cores than GPUs to each virtual machine
- D . Disabling GPU resource allocation limits to maximize performance
- E . Ensuring GPU pass-through capability is enabled in the hypervisor
You are tasked with virtualizing the GPU resources in a multi-tenant AI infrastructure where different teams need isolated access to GPU resources.
Which approach is most suitable for ensuring efficient resource sharing while maintaining isolation between tenants?
- A . NVIDIA vGPU (Virtual GPU) Technology
- B . Deploying Containers Without GPU Isolation
- C . Implementing CPU-based Virtualization
- D . Using GPU Passthrough for Each Tenant
Which of the following best describes a key difference between training and inference architectures in AI deployments?
- A . Inference architectures require distributed training across multiple GPUs.
- B . Training requires higher compute power, while inference prioritizes low latency and high throughput.
- C . Inference requires more memory bandwidth than training.
- D . Training architectures prioritize energy efficiency, while inference architectures do not.
You are working in a high-demand AI environment where multiple deep learning models are being trained simultaneously on a shared NVIDIA GPU cluster. One of the models is experiencing significantly slower training times compared to others, even though it is using the same GPU resources.
Which of the following is the most likely reason for the slowdown?
- A . The model is experiencing resource contention with other models due to improper GPU allocation.
- B . The model has a significantly higher number of parameters compared to the others.
- C . The model’s learning rate is set incorrectly, leading to slower convergence.
- D . The data preprocessing pipeline for this model is underperforming.
A financial services company is using an AI model for fraud detection, deployed on NVIDIA GPUs. After deployment, the company notices a significant delay in processing transactions, which impacts their operations. Upon investigation, it’s discovered that the AI model is being heavily used during peak business hours, leading to resource contention on the GPUs.
What is the best approach to address this issue?
- A . Switch to using CPU resources instead of GPUs for processing.
- B . Disable GPU monitoring to free up resources.
- C . Increase the batch size of input data for the AI model.
- D . Implement GPU load balancing across multiple instances.
You are working on a regression task to predict car prices. Model Gamma has a Mean Absolute Error (MAE) of $1,200, while Model Delta has a Mean Absolute Error (MAE) of $1,500.
Which model should be preferred based on the Mean Absolute Error (MAE), and what does this metric indicate?
- A . Neither model is better because MAE is not suitable for comparing regression models.
- B . Model Delta is better because it has a higher MAE, which means it’s more flexible.
- C . Model Gamma is worse because lower MAE can indicate overfitting.
- D . Model Gamma is better because it has a lower MAE.
In a large-scale AI training environment, a data scientist needs to schedule multiple AI model training jobs with varying dependencies and priorities.
Which orchestration strategy would be most effective to ensure optimal resource utilization and job execution order?
- A . Round-Robin Scheduling
- B . FIFO (First-In-First-Out) Queue
- C . DAG-Based Workflow Orchestration
- D . Manual Scheduling
Your AI project involves large-scale data processing, training complex models, and deploying them to a distributed system.
To efficiently manage the entire lifecycle of the AI project, from data preparation to model monitoring, which combination of NVIDIA software components would best suit this scenario?
- A . NVIDIA Clara Train SDK + NVIDIA Triton Inference Server + NVIDIA DeepOps
- B . NVIDIA Triton Inference Server + NVIDIA RAPIDS
- C . NVIDIA NGC Catalog + NVIDIA DeepOps
- D . NVIDIA Clara + NVIDIA TensorRT