Practice Free NCA-AIIO Exam Online Questions
In a distributed AI training environment, you notice that the GPU utilization drops significantly when the model reaches the backpropagation stage, leading to increased training time.
What is the most effective way to address this issue?
- A . Increase the learning rate to speed up the training process.
- B . Implement mixed-precision training to reduce the computational load during backpropagation.
- C . Optimize the data loading pipeline to ensure continuous GPU data feeding during backpropagation.
- D . Increase the number of layers in the model to create more work for the GPUs during
backpropagation.
An AI research lab is virtualizing its infrastructure to support multiple AI projects concurrently. The operations team needs to ensure that GPU-accelerated applications run smoothly in this virtualized environment.
What are the two key factors they should focus on? (Select two)
- A . Prioritizing network security over GPU resource allocation
- B . Configuring high storage IOPS for each virtual machine
- C . Disabling hyper-threading on CPUs to reduce complexity
- D . Ensuring the hypervisor supports GPU virtualization
- E . Managing GPU allocation based on workload requirements
You are working on an AI project where you need to compare the performance of two different machine learning models: Model A and Model B. Both models are trained to predict house prices based on various features. Model A has a Mean Squared Error (MSE) of 1200, while Model B has a Mean Squared Error (MSE) of 950.
Which model should be considered better based on the Mean Squared Error (MSE), and why?
- A . Neither model is better because MSE is not a relevant metric for comparison.
- B . Model A is better because it might be more stable despite a higher MSE.
- C . Model B is better because it has a lower MSE.
- D . Model A is better because it has a higher MSE.
You are optimizing an AI-driven data center using NVIDIA DPUs to handle increasing network and storage demands. After implementing DPUs, you observe that some applications still suffer from high latency and bottlenecks.
What could be the most likely reason for this issue?
- A . The DPUs are configured to handle storage operations, but the network infrastructure is outdated.
- B . The DPUs are being used to offload AI inference tasks from the GPUs, causing inefficiencies.
- C . The DPUs are not configured to offload sufficient CPU tasks, causing CPU bottlenecks.
- D . The data center is using older-generation GPUs that are not fully compatible with the DPUs.
Your company is developing an AI application that requires seamless integration of data processing, model training, and deployment in a cloud-based environment. The application must support real-time inference and monitoring of model performance.
Which combination of NVIDIA software components is best suited for this end-to-end AI development and deployment process?
- A . NVIDIA RAPIDS + NVIDIA Triton Inference Server + NVIDIA DeepOps
- B . NVIDIA Clara Deploy SDK + NVIDIA Triton Inference Server
- C . NVIDIA RAPIDS + NVIDIA TensorRT
- D . NVIDIA DeepOps + NVIDIA RAPIDS
Which NVIDIA software component is specifically designed to accelerate the end-to-end data science workflow by leveraging GPU acceleration?
- A . NVIDIA RAPIDS
- B . NVIDIA TensorRT
- C . NVIDIA CUDA Toolkit
- D . NVIDIA DeepStream SDK
You are designing a data center platform for a large-scale AI deployment that must handle unpredictable spikes in demand for both training and inference workloads. The goal is to ensure that the platform can scale efficiently without significant downtime or performance degradation.
Which strategy would best achieve this goal?
- A . Implement a round-robin scheduling policy across all servers to distribute workloads evenly.
- B . Migrate all workloads to a single, large cloud instance with multiple GPUs to handle peak loads.
- C . Use a hybrid cloud model with on-premises GPUs for steady workloads and cloud GPUs for scaling during demand spikes.
- D . Deploy a fixed number of high-performance GPU servers with auto-scaling based on CPU usage.
Your company is analyzing large-scale IoT data from thousands of sensors to optimize operational efficiency in real-time. The data is continuously streaming, and you need to identify anomalies that indicate potential system failures. The infrastructure includes NVIDIA GPUs, and the goal is to maximize the performance of your data visualization and anomaly detection tasks.
Which approach would be the most effective for real-time anomaly detection and visualization using the available GPU resources?
- A . Using a GPU-based graph visualization tool to manually identify anomalies.
- B . Running a GPU-accelerated k-means clustering algorithm to group normal and anomalous behavior.
- C . Implementing a GPU-accelerated Convolutional Neural Network (CNN) for anomaly detection.
- D . Applying a simple moving average to detect anomalies in the data stream.
Which of the following statements correctly highlights a key difference between GPU and CPU architectures?
- A . GPUs typically have higher clock speeds than CPUs, allowing them to process individual tasks faster.
- B . CPUs are optimized for parallel processing, making them better for AI workloads, while GPUs are designed for general-purpose tasks.
- C . CPUs are specialized for graphical computations, whereas GPUs handle general-purpose computing tasks.
- D . GPUs are optimized for parallel processing, with thousands of smaller cores, while CPUs have
fewer, more powerful cores optimized for sequential processing.
A large manufacturing company is implementing an AI-based predictive maintenance system to reduce downtime and increase the efficiency of its production lines. The AI system must analyze data from thousands of sensors in real-time to predict equipment failures before they occur. However, during initial testing, the system fails to process the incoming data quickly enough, leading to delayed predictions and occasional missed failures.
What would be the most effective strategy to enhance the system’s real-time processing capabilities?
- A . Increase the frequency of sensor data collection to provide more detailed inputs for the AI model.
- B . Reduce the number of sensors to decrease the amount of data the AI system must process.
- C . Implement edge computing to preprocess sensor data closer to the source before sending it to the central AI system.
- D . Use a more complex AI model to enhance prediction accuracy.