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
You are tasked with optimizing an AI-driven financial modeling application that performs both complex mathematical calculations and real-time data analytics. The calculations are CPU-intensive, requiring precise sequential processing, while the data analytics involves processing large datasets in parallel.
How should you allocate the workloads across GPU and CPU architectures?
- A . Use CPUs for data analytics and GPUs for mathematical calculations.
- B . Use GPUs for mathematical calculations and CPUs for managing I/O operations.
- C . Use CPUs for mathematical calculations and GPUs for data analytics.
- D . Use GPUs for both the mathematical calculations and data analytics.
Which of the following best describes how memory and storage requirements differ between training and inference in AI systems?
- A . Training and inference have identical memory and storage requirements since both involve processing similar data
- B . Inference usually requires more memory than training because of the need to load multiple models simultaneously
- C . Training generally requires more memory and storage due to the need to process large datasets and maintain model states
- D . Training can be done with minimal memory, focusing more on GPU performance, while inference
needs high memory for rapid processing
In which industry has AI most significantly improved operational efficiency through predictive maintenance, leading to reduced downtime and maintenance costs?
- A . Retail
- B . Healthcare
- C . Finance
- D . Manufacturing
An AI operations team is tasked with monitoring a large-scale AI infrastructure where multiple GPUs are utilized in parallel.
To ensure optimal performance and early detection of issues, which two criteria are essential for monitoring the GPUs? (Select two)
- A . Memory bandwidth usage on GPUs
- B . GPU utilization percentage
- C . Number of active CPU threads
- D . GPU fan noise levels
- E . Average CPU temperature
You are managing a team responsible for a large-scale AI project that requires handling and processing vast amounts of data. The data is spread across various storage systems, and the AI models require access to this data for both training and inference.
Which of the following approaches would most
effectively support the efficient management and utilization of data in this AI infrastructure? (Select two)
- A . Limit data access to a single processing node to reduce complexity.
- B . Compress all data before storage to save space.
- C . Implement a distributed file system like Ceph or Hadoop HDFS.
- D . Use a data lake architecture to store and manage data.
- E . Centralize all data in a single, high-performance storage solution.
What is a key consideration when virtualizing accelerated infrastructure to support AI workloads on a hypervisor-based environment?
- A . Ensure GPU passthrough is configured correctly.
- B . Disable GPU overcommitment in the hypervisor.
- C . Enable vCPU pinning to specific cores.
- D . Maximize the number of VMs per physical server.
You are managing a multi-node AI cluster that performs real-time analytics on streaming data from financial markets. The data is processed in parallel across multiple GPUs within a high-performance computing (HPC) environment. Recently, the cluster has been experiencing delays in aggregating the final results, causing the analytics to fall behind market events.
Which action would MOST likely resolve the delay in aggregating results in this HPC environment?
- A . Implementing more aggressive data compression before transmission between nodes.
- B . Increasing the number of GPUs in each node to improve processing speed.
- C . Switching to a batch processing model for handling data streams.
- D . Optimizing the network fabric to reduce latency between the nodes.
Your organization is setting up an AI model deployment pipeline that requires frequent updates. The team needs to ensure minimal downtime during model updates, version control, and monitoring of the models in production.
Which software component would be most suitable to handle these requirements?
- A . NVIDIA Triton Inference Server
- B . NVIDIA TensorRT
- C . NVIDIA DIGITS
- D . NVIDIA NGC Catalog
In an AI data center, you are responsible for monitoring the performance of a GPU cluster used for large-scale model training.
Which of the following monitoring strategies would best help you identify and address performance bottlenecks?
- A . Monitor only the GPU utilization metrics to ensure that all GPUs are being used at full capacity.
- B . Focus on job completion times to ensure that the most critical jobs are being finished on schedule.
- C . Track CPU, GPU, and network utilization simultaneously to identify any resource imbalances that could lead to bottlenecks.
- D . Use predictive analytics to forecast future GPU utilization, adjusting resources before bottlenecks
occur.
You are deploying a machine learning pipeline that includes data preprocessing, model training, and model deployment. Your infrastructure supports both on-demand and scheduled job execution.
How should you configure your MLOps pipeline to ensure that the model is trained and deployed with the most recent data without overloading the system?
- A . Use event-driven triggers to start the pipeline when new data is available.
- B . Schedule all jobs to run every hour, regardless of data updates.
- C . Schedule jobs to run during off-peak hours only, without considering data freshness.
- D . Run the entire pipeline manually whenever new data is available.