NVIDIA NCA-AIIO Practice Exams
Last updated on Oct 22,2026- Exam Code: NCA-AIIO
- Exam Name: AI Infrastructure and Operations
- Certification Provider: NVIDIA
- Latest update: Oct 22,2026
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.
You are working on an AI project that involves training multiple machine learning models to predict customer churn. After training, you need to compare these models to determine which one performs best. The models include a logistic regression model, a decision tree, and a neural network.
Which of the following loss functions and performance metrics would be most appropriate to use for comparing the performance of these models? (Select two)
- A . Mean Squared Error (MSE) for the decision tree model.
- B . Using the proportion of explained variance (R²) for the neural network.
- C . F1-score for comparing model performance on an imbalanced dataset.
- D . Cross-entropy loss for the logistic regression and neural network models.
- E . Accuracy for all models as the sole performance metric.
Your AI data center is running multiple high-performance GPU workloads, and you notice that certain servers are being underutilized while others are consistently at full capacity, leading to inefficiencies.
Which of the following strategies would be most effective in balancing the workload across your AI data center?
- A . Implement NVIDIA GPU Operator with Kubernetes for Automatic Resource Scheduling
- B . Use Horizontal Scaling to Add More Servers
- C . Manually Reassign Workloads Based on Current Utilization
- D . Increase Cooling Capacity in the Data Center
You are comparing two regression models, Model X and Model Y, that predict stock prices. Model X has an R-squared (proportion of explained variance) of 0.75, while Model Y has an R-squared of 0.85.
Which model should you prefer based on the R-squared metric, and what does this metric indicate about the model’s performance?
- A . Model X is better because a lower R-squared indicates more flexibility.
- B . Neither model is better because R-squared is not a reliable metric.
- C . Model Y is better because it has a higher R-squared value, indicating it explains more variance in the data.
- D . Model X is better because it might generalize better despite a lower R-squared.
You are tasked with designing an AI system that needs to handle both training and inference workloads. The system must optimize performance while minimizing costs, given that training is done periodically, but inference is performed continuously in real-time.
What considerations should be made in designing the architecture to meet both training and inference requirements efficiently? (Select two)
- A . Using a mixed-precision approach for both training and inference.
- B . Ensuring redundancy and failover for inference systems.
- C . Deploying GPUs with more memory for inference than for training.
- D . Allocating more GPUs for inference compared to training.
- E . Prioritizing high-bandwidth interconnects between GPUs for training.
You are working on a high-performance AI workload that requires the deployment of deep learning models on a multi-GPU cluster. The workload needs to scale across multiple nodes efficiently while maintaining high throughput and low latency. However, during the deployment, you notice that the GPU utilization is uneven across the nodes, leading to performance bottlenecks.
Which of the following strategies would be the most effective in addressing the uneven GPU utilization in this multi-node AI deployment?
- A . Use a CPU-based load balancer to distribute tasks.
- B . Enable GPU affinity in the job scheduler.
- C . Enable mixed precision training.
- D . Increase the batch size of the workload.
You are managing an AI data center where energy consumption has become a critical concern due to rising costs and sustainability goals. The data center supports various AI workloads, including model training, inference, and data preprocessing.
Which strategy would most effectively reduce energy consumption without significantly impacting performance?
- A . Schedule all AI workloads during nighttime to take advantage of lower electricity rates.
- B . Reduce the clock speed of all GPUs to lower power consumption.
- C . Consolidate all AI workloads onto a single GPU to reduce overall power usage.
- D . Implement dynamic voltage and frequency scaling (DVFS) to adjust GPU power usage based on
real-time workload demands.
In a data center designed for AI workloads, what is a key difference in how GPUs and DPUs complement CPU functionality?
- A . GPUs focus on memory management, whereas DPUs focus on accelerating storage throughput for AI workloads.
- B . GPUs and DPUs are used interchangeably, depending on the specific AI workload, without any direct support from the CPU.
- C . GPUs enhance floating-point computation, while DPUs enhance integer computation, both directly supporting CPU operations.
- D . GPUs are designed for parallel processing of AI models, while DPUs manage data center
infrastructure tasks, complementing CPU operations.
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.
A large manufacturing company is implementing an AI-based predictive maintenance system to reduce downtime and increase the efficiency of its production lines. The AI system must analyze data from thousands of sensors in real-time to predict equipment failures before they occur. However, during initial testing, the system fails to process the incoming data quickly enough, leading to delayed predictions and occasional missed failures.
What would be the most effective strategy to enhance the system’s real-time processing capabilities?
- A . Increase the frequency of sensor data collection to provide more detailed inputs for the AI model.
- B . Reduce the number of sensors to decrease the amount of data the AI system must process.
- C . Implement edge computing to preprocess sensor data closer to the source before sending it to the central AI system.
- D . Use a more complex AI model to enhance prediction accuracy.