A fully updated 2026 NCA-AIIO Exam Dumps exam guide from training expert BraindumpsPass [Q65-Q83]

NEW QUESTION 65
In your AI infrastructure, several GPUs have recently failed during intensive training sessions. To proactively prevent such failures, which GPU metric should you monitor most closely?

NEW QUESTION 66
Your AI cluster handles a mix of training and inference workloads, each with different GPU resource requirements and runtime priorities. What scheduling strategy would best optimize the allocation of GPU resources in this mixed-workload environment?

NEW QUESTION 67
In the context of data center use cases, what is the primary purpose of NVIDIA AI Factories?

NEW QUESTION 68
Your AI team notices that the training jobs on your NVIDIA GPU cluster are taking longer than expected.
Upon investigation, you suspect underutilization of the GPUs. Which monitoring metric is the most critical to determine if the GPUs are being underutilized?

NEW QUESTION 69
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?

NEW QUESTION 70
What is a key architectural difference between AI training and inference?

NEW QUESTION 71
Which NVIDIA technology provides the broadest ecosystem for parallel computation across languages?

NEW QUESTION 72
You are tasked with optimizing the performance of a deep learning model used for image recognition. The model needs to process a large dataset as quickly as possible while maintaining high accuracy. You have access to both GPU and CPU resources. Which two statements best describe why GPUs are more suitable than CPUs for this task? (Select two)

NEW QUESTION 73
You are part of a team that is setting up an AI infrastructure using NVIDIA’s DGX systems. The infrastructure is intended to support multiple AI workloads, including training, inference, and dataanalysis.
You have been tasked with analyzing system logs to identify performance bottlenecks under the supervision of a senior engineer. Which log file would be most useful to analyze when diagnosing GPU performance issues in this scenario?

NEW QUESTION 74
What is the importance of a job scheduler in an AI resource-constrained cluster?

NEW QUESTION 75
A customer is evaluating an AI cluster for training and is questioning why they should use a large number of nodes. Why would multi-node training be advantageous?

NEW QUESTION 76
In a large enterprise cluster, frequent out-of-memory errors occur mid-experiment. What operational feature resolves this?

NEW QUESTION 77
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?

NEW QUESTION 78
What is an important consideration to ensure that proper airflow is provided through a data center rack?

NEW QUESTION 79
Your organization is setting up an AI infrastructure to support a range of AI workloads, including data processing, model training, and inference. The infrastructure needs to be scalable, support distributed training, and handle large datasets efficiently. Which NVIDIA solution would be most suitable for managing and orchestrating this AI infrastructure?

NEW QUESTION 80
In an effort to improve energy efficiency in your AI infrastructure using NVIDIA GPUs, you’re considering several strategies. Which of the following would most effectively balance energy efficiency with maintaining performance?

NEW QUESTION 81
Which of the following statements is true about the difference between GPU and CPU architectures?

NEW QUESTION 82
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?

NEW QUESTION 83
In your AI data center, you’ve observed that some GPUs are underutilized while others are frequently maxed out, leading to uneven performance across workloads. Which monitoring tool or technique would be most effective in identifying and resolving these GPU utilization imbalances?