Practice Free NCA-AIIO Exam Online Questions
You are managing an AI infrastructure using NVIDIA GPUs to train large language models for a social media company. During training, you observe that the GPU utilization is significantly lower than expected, leading to longer training times.
Which of the following actions is most likely to improve GPU utilization and reduce training time?
- A . Increase the batch size during training
- B . Decrease the model complexity
- C . Use mixed precision training
- D . Reduce the learning rate
Which of the following statements best explains a key difference between the infrastructure needs of AI model training and inference?
- A . Training can be performed on edge devices, while inference must always be done in centralized data centers
- B . Inference architectures need more powerful GPUs than training due to real-time processing demands
- C . Inference typically requires more memory and storage than training due to larger batch sizes
- D . Training requires higher precision in computations and more frequent data I/O compared to
inference
Your AI infrastructure team is observing out-of-memory (OOM) errors during the execution of large deep learning models on NVIDIA GPUs.
To prevent these errors and optimize model performance, which GPU monitoring metric is most critical?
- A . Power Usage
- B . PCIe Bandwidth Utilization
- C . GPU Memory Usage
- D . GPU Core Utilization
You manage a large-scale AI infrastructure where several AI workloads are executed concurrently across multiple NVIDIA GPUs. Recently, you observe that certain GPUs are underutilized while others are overburdened, leading to suboptimal performance and extended processing times.
Which of the following strategies is most effective in resolving this imbalance?
- A . Disabling GPU overclocking to normalize performance
- B . Increasing the power limit on underutilized GPUs
- C . Reducing the batch size for all AI workloads
- D . Implementing dynamic GPU load balancing across the infrastructure
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 responsible for managing an AI infrastructure that runs a critical deep learning application. The application experiences intermittent performance drops, especially when processing large datasets. Upon investigation, you find that some of the GPUs are not being fully utilized while others are overloaded, causing the overall system to underperform.
What would be the most effective solution to address the uneven GPU utilization and optimize the performance of the deep learning application?
- A . Reduce the size of the datasets being processed.
- B . Increase the clock speed of the GPUs.
- C . Implement dynamic load balancing for the GPUs.
- D . Add more GPUs to the system.
In a high-performance AI infrastructure, which of the following best describes the role of a Data Processing Unit (DPU) compared to a GPU and CPU?
- A . The DPU primarily accelerates machine learning tasks, similar to the GPU, but is optimized for higher power efficiency.
- B . The DPU is used to optimize graphics rendering tasks in parallel with the GPU.
- C . The DPU offloads network, storage, and security tasks from the CPU, allowing the CPU and GPU to
focus on compute-intensive workloads. - D . The DPU is a specialized processing unit within the GPU that handles deep learning inference.
A healthcare company is deploying an AI model for medical image analysis on an NVIDIA DGX system. The model requires both high compute power and efficient data management to process large datasets quickly. During deployment, the team observes that the storage system is unable to keep up with the high data throughput required by the GPUs, leading to bottlenecks and underutilization of the GPUs.
Which of the following actions would most likely resolve the storage bottleneck and improve GPU utilization?
- A . Implement NVMe over Fabric (NVMe-oF) for faster data transfer
- B . Use a cloud-based storage solution to handle large datasets
- C . Reduce the resolution of the medical images to decrease data size
- D . Switch to traditional spinning disk storage for cost efficiency
During AI model deployment, your team notices significant performance degradation in inference workloads. The model is deployed on an NVIDIA GPU cluster with Kubernetes.
Which of the following could be the most likely cause of the degradation?
- A . High disk I/O latency
- B . Outdated CUDA drivers
- C . CPU bottlenecks
- D . Insufficient GPU memory allocation
A logistics company wants to optimize its delivery routes by predicting traffic conditions and delivery times. The system must process real-time data from various sources, such as GPS, weather reports, and traffic sensors, to adjust routes dynamically.
Which approach should the company use to effectively handle this complex scenario?
- A . Use a rule-based AI system to predefine optimal routes based on historical traffic data
- B . Utilize an unsupervised learning approach to cluster delivery data and generate fixed routes
- C . Apply a basic machine learning algorithm, such as decision trees, to predict delivery times based solely on past delivery records
- D . Implement a deep learning model that uses a convolutional neural network (CNN) to process and
predict traffic conditions based on sensor data