Practice Free NCA-AIIO Exam Online Questions
You are comparing several regression models that predict the future sales of a product based on historical data. The models vary in complexity and computational requirements. Your goal is to select the model that provides the best balance between accuracy and the ability to generalize to new data.
Which performance metric should you prioritize to select the most reliable regression model?
- A . Accuracy
- B . Cross-Entropy Loss
- C . R-squared (Coefficient of Determination)
- D . Mean Squared Error (MSE)
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
Which NVIDIA solution is specifically designed for accelerating and optimizing AI model inference in production environments, particularly for applications requiring low latency?
- A . NVIDIA TensorRT
- B . NVIDIA DGX A100
- C . NVIDIA DeepStream
- D . NVIDIA Omniverse
You are managing a multi-node AI cluster that performs real-time analytics on streaming data from financial markets. The data is processed in parallel across multiple GPUs within a high-performance computing (HPC) environment. Recently, the cluster has been experiencing delays in aggregating the final results, causing the analytics to fall behind market events.
Which action would MOST likely resolve the delay in aggregating results in this HPC environment?
- A . Implementing more aggressive data compression before transmission between nodes.
- B . Increasing the number of GPUs in each node to improve processing speed.
- C . Switching to a batch processing model for handling data streams.
- D . Optimizing the network fabric to reduce latency between the nodes.
A healthcare provider is deploying an AI-driven diagnostic system that analyzes medical images to detect diseases. The system must operate with high accuracy and speed to support doctors in real-time. During deployment, it was observed that the system’s performance degrades when processing high-resolution images in real-time, leading to delays and occasional misdiagnoses.
What should be the primary focus to improve the system’s real-time processing capabilities?
- A . Increase the system’s memory to store more images concurrently.
- B . Use a CPU-based system for image processing to reduce the load on GPUs.
- C . Optimize the AI model’s architecture for better parallel processing on GPUs.
- D . Lower the resolution of input images to reduce the processing load.
Your AI infrastructure needs to support a high-throughput deep learning inference service that must be available 24/7. To improve energy efficiency, you plan to adjust the deployment architecture while maintaining service levels.
Which strategy would be most effective?
- A . Schedule inference tasks to run at lower priority during off-peak hours to conserve energy.
- B . Deploy all inference tasks on lower-power CPUs to reduce overall energy usage.
- C . Implement a static, high-performance GPU cluster that runs at full capacity all the time.
- D . Use an AI-powered workload management system to dynamically allocate GPU resources based on
real-time demand.
In an effort to optimize your data center for AI workloads, you deploy NVIDIA DPUs to offload network and security tasks from CPUs. Despite this, your AI applications still experience high latency during peak processing times.
What is the most likely cause of the latency, and how can it be addressed?
- A . The DPUs are not optimized for AI inference, causing delays in processing tasks that should remain on the CPU or GPU.
- B . The DPUs are offloading too many tasks, leading to underutilization of the CPUs and causing latency.
- C . The network infrastructure is outdated, limiting the effectiveness of the DPUs in reducing latency.
- D . The AI workloads are too large for the DPUs to handle, causing them to slow down other
operations.
Which NVIDIA software component is primarily used to manage and deploy AI models in production environments, providing support for multiple frameworks and ensuring efficient inference?
- A . NVIDIA Triton Inference Server
- B . NVIDIA NGC Catalog
- C . NVIDIA TensorRT
- D . NVIDIA CUDA Toolkit
Your team is developing a predictive maintenance system for a fleet of industrial machines. The system needs to analyze sensor data from thousands of machines in real-time to predict potential failures. You have access to a high-performance AI infrastructure with NVIDIA GPUs and need to implement an approach that can handle large volumes of time-series data efficiently.
Which technique would be most appropriate for extracting insights and predicting machine failures using the available GPU resources?
- A . Applying a GPU-accelerated Long Short-Term Memory (LSTM) network to the time-series data.
- B . Implementing a GPU-accelerated support vector machine (SVM) for classification.
- C . Using a simple linear regression model on a sample of the data.
- D . Visualizing the time-series data using basic line graphs to manually identify trends.
Which components are essential parts of the NVIDIA software stack in an AI environment? (Select two)
- A . NVIDIA GameWorks
- B . NVIDIA CUDA Toolkit
- C . NVIDIA TensorRT
- D . NVIDIA Nsight Systems
- E . NVIDIA JetPack SDK