NVIDIA NCA-AIIO Practice Exams
Last updated on Oct 23,2026- Exam Code: NCA-AIIO
- Exam Name: AI Infrastructure and Operations
- Certification Provider: NVIDIA
- Latest update: Oct 23,2026
In an AI infrastructure setup, you need to optimize the network for high-performance data movement between storage systems and GPU compute nodes.
Which protocol would be most effective for achieving low latency and high bandwidth in this environment?
- A . TCP/IP
- B . Remote Direct Memory Access (RDMA)
- C . SMTP
- D . HTTP
When implementing an MLOps pipeline, which component is crucial for managing version control and tracking changes in model experiments?
- A . Artifact Repository
- B . Continuous Integration (CI) System
- C . Orchestration Platform
- D . Model Registry
In your multi-tenant AI cluster, multiple workloads are running concurrently, leading to some jobs experiencing performance degradation.
Which GPU monitoring metric is most critical for identifying resource contention between jobs?
- A . GPU Utilization Across Jobs
- B . GPU Temperature
- C . Memory Bandwidth Utilization
- D . Network Latency
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.
Your AI team is running a distributed deep learning training job on an NVIDIA DGX A100 cluster using multiple nodes. The training process is slowing down significantly as the model size increases.
Which of the following strategies would be most effective in optimizing the training performance?
- A . Enable Mixed Precision Training
- B . Increase Batch Size
- C . Decrease the Number of Nodes
- D . Use Data Parallelism Instead of Model Parallelism
A large enterprise is deploying a high-performance AI infrastructure to accelerate its machine learning workflows. They are using multiple NVIDIA GPUs in a distributed environment.
To optimize the workload distribution and maximize GPU utilization, which of the following tools or frameworks should be integrated into their system? (Select two)
- A . Keras
- B . TensorFlow Serving
- C . NVIDIA CUDA
- D . NVIDIA NGC (NVIDIA GPU Cloud)
- E . NVIDIA NCCL (NVIDIA Collective Communications Library)
After deploying an AI model on an NVIDIA T4 GPU in a production environment, you notice that the inference latency is inconsistent, varying significantly during different times of the day.
Which of the following actions would most likely resolve the issue?
- A . Deploy the Model on a CPU Instead of a GPU
- B . Implement GPU Isolation for the Inference Process
- C . Increase the Number of Inference Threads
- D . Upgrade the GPU Driver
Which components are essential parts of the NVIDIA software stack in an AI environment? (Select two)
- A . NVIDIA GameWorks
- B . NVIDIA CUDA Toolkit
- C . NVIDIA TensorRT
- D . NVIDIA Nsight Systems
- E . NVIDIA JetPack SDK
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 have developed two different machine learning models to predict house prices based on various features like location, size, and number of bedrooms. Model A uses a linear regression approach, while Model B uses a random forest algorithm. You need to compare the performance of these models to determine which one is better for deployment.
Which two statistical performance metrics would be most appropriate to compare the accuracy and reliability of these models? (Select two)
- A . Mean Absolute Error (MAE)
- B . Cross-Entropy Loss
- C . F1 Score
- D . R-squared (Coefficient of Determination)
- E . Learning Rate