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
Which NVIDIA solution is specifically designed to accelerate the development and deployment of AI in healthcare, particularly in medical imaging and genomics?
- A . NVIDIA TensorRT
- B . NVIDIA Metropolis
- C . NVIDIA Jetson
- D . NVIDIA Clara
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
You have deployed an AI training job on a GPU cluster, but the training time has not decreased as expected after adding more GPUs. Upon further investigation, you observe that the GPU utilization is low, and the CPU utilization is very high.
What is the most likely cause of this issue?
- A . Incorrect software version installed on the GPUs.
- B . The GPUs are not properly connected in the cluster.
- C . The data preprocessing is being bottlenecked by the CPU.
- D . The AI model is not compatible with multi-GPU training.
You are tasked with optimizing the training process of a deep learning model on a multi-GPU setup. Despite having multiple GPUs, the training is slow, and some GPUs appear to be idle.
What is the most likely reason for this, and how can you resolve it?
- A . The data is too large, and the CPU is not powerful enough to handle the pre-processing.
- B . The model architecture is too simple to utilize multiple GPUs effectively.
- C . The GPUs have insufficient memory to handle the dataset, leading to slow processing.
- D . The GPUs are not properly synchronized, causing some GPUs to wait for others.
During routine monitoring of your AI data center, you notice that several GPU nodes are consistently reporting high memory usage but low compute usage.
What is the most likely cause of this situation?
- A . The power supply to the GPU nodes is insufficient.
- B . The data being processed includes large datasets that are stored in GPU memory but not efficiently utilized in computation.
- C . The workloads are being run with models that are too small for the available GPUs.
- D . The GPU drivers are outdated and need updating.
Your organization is planning to deploy an AI solution that involves large-scale data processing, training, and real-time inference in a cloud environment. The solution must ensure seamless integration of data pipelines, model training, and deployment.
Which combination of NVIDIA software components will best support the entire lifecycle of this AI solution?
- A . NVIDIA TensorRT + NVIDIA DeepStream SDK
- B . NVIDIA RAPIDS + NVIDIA TensorRT
- C . NVIDIA Triton Inference Server + NVIDIA NGC Catalog
- D . NVIDIA RAPIDS + NVIDIA Triton Inference Server + NVIDIA NGC Catalog
When comparing and contrasting the architectural requirements of AI training and inference, which of the following statements are correct? (Select three)
- A . Inference architectures typically require more frequent data transfer between storage and compute compared to training architectures.
- B . Inference architectures are more likely to require high availability and fault tolerance than training architectures.
- C . Training is typically more memory-intensive than inference, requiring larger GPU memory.
- D . Training architectures typically require higher computational power than inference architectures.
- E . Training architectures usually need to be more scalable than inference architectures.
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)
- A . CPUs are better suited for handling the large dataset due to their superior memory bandwidth.
- B . CPUs consume less power than GPUs, making them more suitable for prolonged computations.
- C . GPUs are optimized for matrix operations, which are common in deep learning algorithms.
- D . GPUs have a higher number of cores compared to CPUs, allowing for parallel processing of many tasks simultaneously.
- E . GPUs have a lower latency than CPUs, making them faster for individual calculations.
You are working on a project that involves both real-time AI inference and data pre-processing tasks. The AI models require high throughput and low latency, while the data pre-processing involves complex logic and diverse data types. Given the need to balance these tasks, which computing architecture should you prioritize for each task?
- A . Use CPUs for both AI inference and data pre-processing.
- B . Use GPUs for both AI inference and data pre-processing.
- C . Prioritize GPUs for AI inference and CPUs for data pre-processing.
- D . Deploy AI inference on CPUs and data pre-processing on FPGAs.
You are tasked with deploying an AI model to a production environment that requires high availability, scalability, and continuous integration/continuous delivery (CI/CD) pipelines. The deployment needs to ensure minimal downtime during updates and seamless scaling based on demand.
Which two software components are essential to manage the deployment and scaling of the AI model in this scenario? (Select two)
- A . Apache Hadoop
- B . TensorFlow
- C . Apache Kafka
- D . Docker
- E . Kubernetes