Practice Free NCA-AIIO Exam Online Questions
In which industry has AI most significantly improved operational efficiency through predictive maintenance, leading to reduced downtime and maintenance costs?
- A . Retail
- B . Healthcare
- C . Finance
- D . Manufacturing
You are managing an AI data center where multiple GPUs are orchestrated across a large cluster to run various deep learning tasks.
Which of the following actions best describes an efficient approach to cluster orchestration in this environment?
- A . Assign all jobs to the most powerful GPU in the cluster to maximize performance and minimize job duration.
- B . Implement a Kubernetes-based orchestration system to dynamically allocate GPU resources based on workload requirements and GPU availability.
- C . Use a round-robin scheduling algorithm to distribute jobs evenly across all GPUs, regardless of their individual workloads.
- D . Prioritize job assignments to GPUs with the least power consumption to reduce energy costs.
Your company is implementing a hybrid cloud AI infrastructure that needs to support both on-premises and cloud-based AI workloads. The infrastructure must enable seamless integration, scalability, and efficient resource management across different environments.
Which NVIDIA solution should be considered to best support this hybrid infrastructure?
- A . NVIDIA Clara Deploy SDK
- B . NVIDIA MIG (Multi-Instance GPU)
- C . NVIDIA Fleet Command
- D . NVIDIA Triton Inference Server
A telecommunications company is rolling out an AI-based system to optimize network traffic and improve customer experience across multiple regions. The system must process real-time data from millions of devices, predict network congestion, and dynamically adjust resource allocation. The infrastructure needs to ensure low latency, high availability, and the ability to scale as the network expands.
Which NVIDIA technologies would best support the deployment of this AI-based network optimization system?
- A . Deploy the system on NVIDIA Tesla P100 GPUs with TensorFlow Serving for inference.
- B . Implement the system using NVIDIA Jetson Xavier NX for edge computing at regional network hubs.
- C . Use NVIDIA BlueField-2 DPUs for offloading networking tasks and NVIDIA DOCA SDK for orchestration.
- D . Utilize NVIDIA DGX-1 with CUDA for training AI models and deploy them on CPU-based servers.
You are responsible for managing an AI infrastructure where multiple data scientists are simultaneously
running large-scale training jobs on a shared GPU cluster. One data scientist reports that their training job is running much slower than expected, despite being allocated sufficient GPU resources. Upon investigation, you notice that the storage I/O on the system is consistently high.
What is the most likely cause of the slow performance in the data scientist’s training job?
- A . Insufficient GPU memory allocation
- B . Inefficient data loading from storage
- C . Incorrect CUDA version installed
- D . Overcommitted CPU resources
Your AI infrastructure team is deploying a large NLP model on a Kubernetes cluster using NVIDIA GPUs. The model inference requires low latency due to real-time user interaction. However, the team notices occasional latency spikes.
What would be the most effective strategy to mitigate these latency spikes?
- A . Deploy the Model on Multi-Instance GPU (MIG) Architecture
- B . Use NVIDIA Triton Inference Server with Dynamic Batching
- C . Increase the Number of Replicas in the Kubernetes Cluster
- D . Reduce the Model Size by Quantization
You are assisting in a project that involves deploying a large-scale AI model on a multi-GPU server. The server is experiencing unexpected performance degradation during inference, and you have been asked to analyze the system under the supervision of a senior engineer.
Which approach would be most effective in identifying the source of the performance degradation?
- A . Check the system’s CPU utilization.
- B . Inspect the training data for inconsistencies.
- C . Analyze the GPU memory usage using nvidia-smi.
- D . Monitor the system’s power supply levels.
You are responsible for managing an AI data center that supports various AI workloads, including training, inference, and data processing.
Which two practices are essential for ensuring optimal resource utilization and minimizing downtime? (Select two)
- A . Regularly monitoring and updating firmware on GPUs and other hardware
- B . Disabling alerts for non-critical issues to reduce alert fatigue
- C . Limiting the use of virtualization to reduce overhead
- D . Running all AI workloads during peak usage hours to maximize efficiency
- E . Implementing automated workload scheduling based on resource availability
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
Which networking feature is MOST important for supporting distributed training of large AI models across multiple data centers?
- A . Deployment of wireless networking to enable flexible node placement.
- B . Segregated network segments to prevent data leakage between AI tasks.
- C . Implementation of Quality of Service (QoS) policies to prioritize AI training traffic.
- D . High throughput with low latency WAN links between data centers.