Practice Free NCA-AIIO Exam Online Questions
The operations team is tasked with ensuring the reliability and efficiency of an AI data center that handles critical workloads.
What are two essential practices they should implement for effective AI data center management and monitoring? (Select two)
- A . Schedule all AI workload deployments during non-peak hours to reduce strain on the infrastructure.
- B . Optimize data storage by minimizing the frequency of backups to reduce costs.
- C . Regularly audit GPU utilization and thermal metrics using NVIDIA DCGM (Data Center GPU Manager).
- D . Use a general-purpose monitoring tool without AI-specific capabilities.
- E . Implement redundant power supplies and cooling systems.
You are managing a high-performance AI infrastructure that leverages NVIDIA GPUs for deep learning training workloads. Your team is experiencing significant bottlenecks in data loading, causing GPUs to be underutilized during training sessions. You suspect that the storage solution might be contributing to the issue.
Which of the following changes would most likely optimize GPU utilization and reduce data loading bottlenecks?
- A . Upgrade to a faster storage solution like NVMe SSDs.
- B . Enable data augmentation to increase dataset variety.
- C . Reduce the batch size in your training jobs.
- D . Increase the number of GPUs in the system.
Which of the following NVIDIA compute platforms is best suited for deploying AI workloads at the edge with minimal latency?
- A . NVIDIA Jetson
- B . NVIDIA Tesla
- C . NVIDIA RTX
- D . NVIDIA GRID
You are managing an AI infrastructure that includes multiple NVIDIA GPUs across various virtual machines (VMs) in a cloud environment. One of the VMs is consistently underperforming compared to others, even though it has the same GPU allocation and is running similar workloads.
What is the most likely cause of the underperformance in this virtual machine?
- A . Inadequate storage I/O performance
- B . Insufficient CPU allocation for the VM
- C . Misconfigured GPU passthrough settings
- D . Incorrect GPU driver version installed
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.
You are tasked with deploying an AI model across multiple cloud providers, each using NVIDIA GPUs. During the deployment, you observe that the model’s performance varies significantly between the providers, even though identical instance types and configurations are used.
What is the most likely reason for this discrepancy?
- A . Differences in the GPU architecture between the cloud providers
- B . Different versions of the AI framework being used across providers
- C . Cloud providers using different cooling systems for their data centers
- D . Variations in cloud provider-specific optimizations and software stack
You are responsible for deploying a deep learning model for image recognition across a global network of autonomous vehicles. The model requires real-time inference and must be updated frequently with new training data. The deployment needs to be highly efficient in both edge and cloud environments, with minimal latency.
Which combination of NVIDIA technologies would best meet the requirements for this deployment?
- A . Use NVIDIA Jetson AGX Xavier for edge inference and NVIDIA Fleet Command for centralized management.
- B . Use NVIDIA Quadro RTX GPUs on each vehicle for real-time inference.
- C . Utilize NVIDIA Tesla V100 GPUs in the cloud with TensorRT for optimized inference.
- D . Deploy the model on NVIDIA DGX systems located in data centers for real-time inference.
Your team is tasked with analyzing a large financial dataset consisting of transaction records from millions of customers. The goal is to detect patterns indicative of fraudulent activities. The dataset is highly imbalanced, with a small number of fraudulent transactions compared to legitimate ones. You have access to an NVIDIA GPU-powered infrastructure.
Which approach would be most effective in identifying fraudulent transactions in this large, imbalanced dataset?
- A . Employing standard logistic regression without GPU acceleration.
- B . Using a GPU-accelerated SMOTE (Synthetic Minority Over-sampling Technique) before training a model.
- C . Filtering out all non-fraudulent transactions and focusing solely on fraudulent ones.
- D . Applying a GPU-accelerated Random Forest algorithm without any pre-processing.
Which NVIDIA solution is specifically designed for simulating complex, large-scale AI workloads in a multi-user environment, particularly for collaborative projects in industries like robotics, manufacturing, and entertainment?
- A . NVIDIA JetPack
- B . NVIDIA TensorRT
- C . NVIDIA Triton Inference Server
- D . NVIDIA Omniverse
Your organization operates an AI cluster where various deep learning tasks are executed. Some tasks are time-sensitive and must be completed as soon as possible, while others are less critical. Additionally, some jobs can be parallelized across multiple GPUs, while others cannot. You need to implement a job scheduling policy that balances these needs effectively.
Which scheduling policy would best balance the needs of time-sensitive tasks and efficiently utilize the available GPUs?
- A . Implement a priority-based scheduling system that also considers GPU availability and task parallelizability
- B . Use a round-robin scheduling approach to ensure equal access for all jobs
- C . First-Come, First-Served (FCFS) scheduling to maintain order
- D . Schedule the longest-running jobs first to reduce overall cluster load