Practice Free NCA-AIIO Exam Online Questions
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.
You are managing a machine learning pipeline in an AI data center where the training jobs are scheduled across multiple GPU clusters. Recently, you’ve noticed that certain training jobs are frequently delayed or rescheduled, leading to inconsistencies in model updates. After investigating, you find that these jobs often compete for the same resources.
What is the most effective way to address this issue?
- A . Implement resource quotas to ensure fair allocation across jobs.
- B . Increase the priority of the delayed jobs in the scheduling queue.
- C . Disable job preemption to prevent jobs from being rescheduled.
- D . Add more GPUs to the clusters to reduce contention.
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 data center is experiencing fluctuating workloads where some AI models require significant computational resources at specific times, while others have a steady demand.
Which of the following resource management strategies would be most effective in ensuring efficient use of GPU resources across varying workloads?
- A . Manually Schedule Workloads Based on Expected Demand
- B . Use Round-Robin Scheduling for Workloads
- C . Upgrade All GPUs to the Latest Model
- D . Implement NVIDIA MIG (Multi-Instance GPU) for Resource Partitioning
In your AI infrastructure, several GPUs have recently failed during intensive training sessions.
To proactively prevent such failures, which GPU metric should you monitor most closely?
- A . Power Consumption
- B . GPU Temperature
- C . GPU Driver Version
- D . Frame Buffer Utilization
Which of the following statements best describes the difference between infrastructure requirements for training and inference in AI architectures?
- A . Inference requires distributed computing clusters, while training can be done on a single server
- B . Training requires more computational resources and memory bandwidth compared to inference
- C . Training typically runs on edge devices, while inference is run in centralized data centers
- D . Inference always requires more GPUs than training
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.
You are working with a large dataset containing millions of records related to customer behavior. Your goal is to identify key trends and patterns that could improve your company’s product recommendations. You have access to a high-performance AI infrastructure with NVIDIA GPUs, and you want to leverage this for efficient data mining.
Which technique would most effectively utilize the GPUs to extract actionable insights from the dataset?
- A . Implementing deep learning models for clustering customers into segments.
- B . Using traditional SQL queries to filter and sort the data.
- C . Visualizing the data using a standard spreadsheet application.
- D . Employing a simple decision tree model to classify customer data.
You are supporting a senior engineer in troubleshooting an AI workload that involves real-time data processing on an NVIDIA GPU cluster. The system experiences occasional slowdowns during data ingestion, affecting the overall performance of the AI model.
Which approach would be most effective in diagnosing the cause of the data ingestion slowdown?
- A . Profile the I/O operations on the storage system.
- B . Optimize the AI model’s inference code.
- C . Switch to a different data preprocessing framework.
- D . Increase the number of GPUs used for data processing.
Your organization is setting up an AI model deployment pipeline that requires frequent updates. The team needs to ensure minimal downtime during model updates, version control, and monitoring of the models in production.
Which software component would be most suitable to handle these requirements?
- A . NVIDIA Triton Inference Server
- B . NVIDIA TensorRT
- C . NVIDIA DIGITS
- D . NVIDIA NGC Catalog