Practice Free NCA-AIIO Exam Online Questions
A company is implementing a new AI-based recommendation system for its e-commerce platform, which handles millions of users and transactions daily. The company plans to deploy this system on an NVIDIA DGX platform to achieve the required performance. During initial testing, the AI model performs well on smaller datasets but struggles to handle the full-scale production data, leading to increased latency and degraded user experience.
Which of the following would be the most effective approach to resolve this issue?
- A . Scale the number of GPUs on the DGX platform to increase computational power.
- B . Replace the DGX platform with a traditional CPU-based server to handle the larger data volume.
- C . Decrease the batch size during inference to reduce memory usage.
- D . Use a smaller, simpler AI model to reduce computational requirements.
You are tasked with creating a real-time dashboard for monitoring the performance of a large-scale AI system processing social media data. The dashboard should provide insights into trends, anomalies, and performance metrics using NVIDIA GPUs for data processing and visualization.
Which tool or technique would most effectively leverage the GPU resources to visualize real-time insights from this high-volume social media data?
- A . Employing a GPU-accelerated time-series database for real-time data ingestion and visualization.
- B . Using a standard CPU-based ETL (Extract, Transform, Load) process to prepare the data for visualization.
- C . Relying solely on a relational database to handle the data and generate visualizations.
- D . Implementing a GPU-accelerated deep learning model to generate insights and feeding results
directly into the dashboard.
Your AI infrastructure involves running diverse workloads, including real-time inference and long-term training jobs. To manage these effectively, which two job scheduling techniques should you prioritize? (Select two)
- A . Disable dynamic scheduling to reduce the overhead associated with managing job queues
- B . Allocate fixed resources for each job to avoid competition
- C . Prioritize real-time inference jobs over long-term training jobs using job prioritization policies
- D . Schedule all jobs sequentially to ensure fairness in resource allocation
- E . Implement preemptive scheduling to allow higher-priority jobs to interrupt lower-priority ones
What is a key consideration when virtualizing accelerated infrastructure to support AI workloads on a hypervisor-based environment?
- A . Ensure GPU passthrough is configured correctly.
- B . Disable GPU overcommitment in the hypervisor.
- C . Enable vCPU pinning to specific cores.
- D . Maximize the number of VMs per physical server.
In an AI data center, you are working with a professional administrator to optimize the deployment of AI workloads across multiple servers.
Which of the following actions would best contribute to improving the efficiency and performance of the data center?
- A . Distribute AI workloads across multiple servers with GPUs, while using DPUs to manage network traffic and storage access.
- B . Allocate all networking tasks to the CPUs, allowing the GPUs and DPUs to focus solely on AI model processing.
- C . Consolidate all AI workloads onto a single high-performance server to maximize GPU utilization.
- D . Use the CPUs exclusively for AI training tasks while GPUs and DPUs handle background
operations.
You are tasked with deploying multiple AI workloads in a data center that supports both virtualized and non-virtualized environments.
To maximize resource efficiency and flexibility, which of the following strategies would be most effective for running AI workloads in a virtualized environment?
- A . Deploy each AI workload in a separate virtual machine (VM) to isolate resources and prevent interference between workloads.
- B . Run all AI workloads on bare metal servers without virtualization to maximize performance.
- C . Use containerization within a single VM to run multiple AI workloads, leveraging shared resources while maintaining isolation.
- D . Use a single VM to run all AI workloads sequentially, reducing the need for resource scheduling.
In your AI data center, you need to ensure continuous performance and reliability across all operations.
Which two strategies are most critical for effective monitoring? (Select two)
- A . Implementing predictive maintenance based on historical hardware performance data
- B . Using manual logs to track system performance daily
- C . Conducting weekly performance reviews without real-time monitoring
- D . Disabling non-essential monitoring to reduce system overhead
- E . Deploying a comprehensive monitoring system that includes real-time metrics on CPU, GPU,
memory, and network usage
You are tasked with optimizing the performance of a distributed deep learning workload running on multiple GPU nodes in your data center. After monitoring the system, you notice that the network latency between the nodes is unusually high, leading to increased training times.
What is the most effective action to take to resolve this issue?
- A . Use a different deep learning framework that requires less communication between nodes.
- B . Upgrade to faster GPUs with more memory.
- C . Increase the number of CPU cores on each node.
- D . Reconfigure the data distribution strategy to minimize cross-node communication.
A financial services company is developing a machine learning model to detect fraudulent transactions in real-time. They need to manage the entire AI lifecycle, from data preprocessing to model deployment and monitoring.
Which combination of NVIDIA software components should they integrate to ensure an efficient and scalable AI development and deployment process?
- A . NVIDIA Metropolis for data collection, DIGITS for training, and Triton Inference Server for deployment.
- B . NVIDIA Clara for model training, TensorRT for data processing, and Jetson for deployment.
- C . NVIDIA DeepStream for data processing, CUDA for model training, and NGC for deployment.
- D . NVIDIA RAPIDS for data processing, TensorRT for model optimization, and Triton Inference Server
for deployment.
You are part of a team analyzing the results of an AI model training process across various hardware configurations. The objective is to determine how different hardware factors, such as GPU type, memory size, and CPU-GPU communication speed, affect the model’s training time and final accuracy.
Which analysis method would best help in identifying trends or relationships between hardware factors and model performance?
- A . Conduct a regression analysis with hardware factors as independent variables and model performance as dependent variables.
- B . Create a heatmap of CPU-GPU communication speed versus training time.
- C . Plot a scatter plot of model performance against GPU type.
- D . Use a bar chart to compare the average training times across different hardware configurations.