Practice Free NCA-AIIO Exam Online Questions
Your company is running a distributed AI application that involves real-time data ingestion from IoT devices spread across multiple locations. The AI model processing this data requires high throughput and low latency to deliver actionable insights in near real-time. Recently, the application has been experiencing intermittent delays and data loss, leading to decreased accuracy in the AI model’s predictions.
Which action would BEST improve the performance and reliability of the AI application in this scenario?
- A . Implementing a dedicated, high-bandwidth network link between IoT devices and the data processing centers.
- B . Switching to a batch processing model to reduce the frequency of data transfers.
- C . Upgrading the IoT devices to more powerful hardware.
- D . Deploying a Content Delivery Network (CDN) to cache data closer to the IoT devices.
You are working under the supervision of a senior data scientist to analyze the results of a large-scale machine learning experiment. The experiment produced a vast amount of data, and your task is to create visualizations that effectively convey the key findings to stakeholders. The stakeholders are particularly interested in understanding the model’s performance across different categories.
Which type of visualization would be most appropriate for comparing the model’s performance across multiple categories?
- A . Line chart showing performance over time.
- B . Scatter plot showing relationships between two variables.
- C . Pie chart showing the distribution of categories.
- D . Bar chart with error bars for each category.
An AI research team is working on a large-scale natural language processing (NLP) model that requires both data preprocessing and training across multiple GPUs. They need to ensure that the GPUs are used efficiently to minimize training time.
Which combination of NVIDIA technologies should they use?
- A . NVIDIA DeepStream SDK and NVIDIA CUDA Toolkit
- B . NVIDIA DALI (Data Loading Library) and NVIDIA NCCL
- C . NVIDIA TensorRT and NVIDIA DGX OS
- D . NVIDIA cuDNN and NVIDIA NGC Catalog
You are managing a large-scale AI cluster designed to handle multiple deep learning workloads concurrently.
To ensure scalability and efficient resource usage, which two practices should you implement in your orchestration strategy? (Select two)
- A . Implement horizontal scaling to add more nodes to the cluster as demand increases
- B . Use manual job scheduling to precisely control resource allocation
- C . Use Kubernetes to automate the deployment and scaling of containerized workloads
- D . Disable load balancing to reduce complexity in job distribution
- E . Schedule all jobs to run on the same node to minimize inter-node communication
Your team is building an AI-powered application that requires the deployment of multiple models, each trained using different frameworks (e.g., TensorFlow, PyTorch, and ONNX). You need a deployment solution that can efficiently serve all these models in production, regardless of the framework they were built in.
Which software component should you choose?
- A . NVIDIA Triton Inference Server
- B . NVIDIA TensorRT
- C . NVIDIA Clara Deploy SDK
- D . NVIDIA DeepOps
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
A large healthcare provider wants to implement an AI-driven diagnostic system that can analyze medical images across multiple hospitals. The system needs to handle large volumes of data, comply with strict data privacy regulations, and provide fast, accurate results. The infrastructure should also support future scaling as more hospitals join the network.
Which approach using NVIDIA technologies would best meet the requirements for this AI-driven diagnostic system?
- A . Use NVIDIA Jetson Nano devices at each hospital for image processing.
- B . Deploy the AI model on NVIDIA DGX A100 systems in a centralized data center with NVIDIA Clara for healthcare-specific AI tools.
- C . Deploy the system using generic CPU servers with TensorFlow for model training and inference.
- D . Implement the AI system on NVIDIA Quadro RTX GPUs across local servers in each hospital.
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
What is a key value of using NVIDIA NIMs?
- A . They provide fast and simple deployment of AI models.
- B . They have community support.
- C . They allow the deployment of NVIDIA SDKs.
A
Explanation:
NVIDIA NIMs (NVIDIA Inference Microservices) are pre-built, GPU-accelerated microservices with standardized APIs, designed to simplify and accelerate AI model deployment across diverse environments―clouds, data centers, and edge devices. Their key value lies in enabling fast, turnkey inference without requiring custom deployment pipelines, reducing setup time and complexity. While community support and SDK deployment may be tangential benefits, they are not the primary focus of NIMs.
(Reference: NVIDIA NIMs Documentation, Overview Section)
Which NVIDIA hardware and software combination is most appropriate for developing and deploying AI models in a high-performance, data center environment?
- A . NVIDIA Jetson Nano with TensorFlow for model development.
- B . NVIDIA Quadro RTX 6000 with RAPIDS for data analytics.
- C . NVIDIA DGX A100 with PyTorch and CUDA for model development and deployment.
- D . NVIDIA T4 GPUs with TensorFlow Lite for model deployment.