Practice Free NCA-AIIO Exam Online Questions
You are managing an AI cluster with several nodes, each equipped with multiple NVIDIA GPUs. The cluster supports various machine learning tasks with differing resource requirements. Some jobs are GPU-intensive, while others require high memory but minimal GPU usage. Your goal is to efficiently allocate resources to maximize throughput and minimize job wait times.
Which orchestration strategy would best optimize resource allocation in this mixed-workload environment?
- A . Use a dynamic scheduler that adjusts resource allocation based on job requirements and current cluster utilization
- B . Manually assign jobs to specific nodes based on estimated workload requirements
- C . Schedule jobs based on a fixed priority order, regardless of resource requirements
- D . Allocate GPUs evenly across all jobs to ensure fair distribution
During a high-intensity AI training session on your NVIDIA GPU cluster, you notice a sudden drop in performance.
Suspecting thermal throttling, which GPU monitoring metric should you prioritize to confirm this issue?
- A . GPU Clock Speed
- B . Memory Bandwidth Utilization
- C . GPU Temperature and Thermal Status
- D . CPU Utilization
When virtualizing a GPU-accelerated infrastructure, which of the following is a critical consideration to ensure optimal performance for AI workloads?
- A . Ensuring proper NUMA (Non-Uniform Memory Access) alignment
- B . Using software-based GPU virtualization instead of hardware passthrough
- C . Maximizing the number of VMs per GPU
- D . Allocating more virtual CPUs (vCPUs) than physical CPUs
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
A data science team compares two regression models for predicting housing prices. Model X has an R-squared value of 0.85, while Model Y has an R-squared value of 0.78. However, Model Y has a lower Mean Absolute Error (MAE) than Model X.
Based on these statistical performance metrics, which model should be chosen for deployment, and why?
- A . Model X should be chosen because it is likely to perform better on unseen data.
- B . Model X should be chosen because a higher R-squared value indicates it explains more variance in the data.
- C . Model Y should be chosen because a lower MAE indicates it has better prediction accuracy.
- D . Model X should be chosen because R-squared is a more comprehensive metric than MAE.
Your AI team is using Kubernetes to orchestrate a cluster of NVIDIA GPUs for deep learning training jobs. Occasionally, some high-priority jobs experience delays because lower-priority jobs are consuming GPU resources.
Which of the following actions would most effectively ensure that high-priority jobs are allocated GPU resources first?
- A . Increase the Number of GPUs in the Cluster
- B . Configure Kubernetes Pod Priority and Preemption
- C . Manually Assign GPUs to High-Priority Jobs
- D . Use Kubernetes Node Affinity to Bind Jobs to Specific Nodes
You are deploying a large-scale AI model training pipeline on a cloud-based infrastructure that uses NVIDIA GPUs. During the training, you observe that the system occasionally crashes due to memory overflows on the GPUs, even though the overall GPU memory usage is below the maximum capacity.
What is the most likely cause of the memory overflows, and what should you do to mitigate this issue?
- A . The model’s batch size is too large; reduce the batch size.
- B . The system is encountering fragmented memory; enable unified memory management.
- C . The GPUs are not receiving data fast enough; increase the data pipeline speed.
- D . The CPUs are overloading the GPUs; allocate more CPU cores to handle preprocessing.
Which component of the NVIDIA AI software stack is primarily responsible for optimizing deep learning inference performance by leveraging the specific architecture of NVIDIA GPUs?
- A . NVIDIA CUDA Toolkit
- B . NVIDIA TensorRT
- C . NVIDIA cuDNN
- D . NVIDIA Triton Inference Server
You are assisting a senior engineer in analyzing the results of an experiment that tested multiple configurations of an AI model on different datasets. The goal is to identify any relationships between the dataset characteristics (such as size, type, and quality) and the model’s performance metrics. You need to find which factors most significantly impact the model’s accuracy.
Which approach would be most effective for identifying the factors that most significantly impact the model’s accuracy?
- A . Apply PCA (Principal Component Analysis) on the model’s performance metrics.
- B . Create a histogram of accuracy scores for each dataset.
- C . Use a pie chart to display the proportion of datasets that achieved high accuracy.
- D . Perform a correlation analysis between dataset characteristics and accuracy.
You are managing a data center running numerous AI workloads on NVIDIA GPUs. Recently, some of the GPUs have been showing signs of underperformance, leading to slower job completion times. You suspect that resource utilization is not optimal. You need to implement monitoring strategies to ensure GPUs are effectively utilized and to diagnose any underperformance.
Which of the following metrics is most critical to monitor for identifying underutilized GPUs in your data center?
- A . Network Bandwidth Utilization
- B . System Uptime
- C . GPU Memory Usage
- D . GPU Core Utilization