Practice Free NCA-AIIO Exam Online Questions
You are supporting a senior engineer in troubleshooting an AI workload that involves real-time data processing on an NVIDIA GPU cluster. The system experiences occasional slowdowns during data ingestion, affecting the overall performance of the AI model.
Which approach would be most effective in diagnosing the cause of the data ingestion slowdown?
- A . Profile the I/O operations on the storage system.
- B . Optimize the AI model’s inference code.
- C . Switch to a different data preprocessing framework.
- D . Increase the number of GPUs used for data processing.
A company is using a multi-GPU server for training a deep learning model. The training process is extremely slow, and after investigation, it is found that the GPUs are not being utilized efficiently. The system uses NVLink, and the software stack includes CUDA, cuDNN, and NCCL.
Which of the following actions is most likely to improve GPU utilization and overall training performance?
- A . Increase the batch size
- B . Update the CUDA version to the latest release
- C . Disable NVLink and use PCIe for inter-GPU communication
- D . Optimize the model’s code to use mixed-precision training
A financial institution is using an NVIDIA DGX SuperPOD to train a large-scale AI model for real-time fraud detection. The model requires low-latency processing and high-throughput data management. During the training phase, the team notices significant delays in data processing, causing the GPUs to idle frequently. The system is configured with NVMe storage, and the data pipeline involves DALI (Data Loading Library) and RAPIDS for preprocessing.
Which of the following actions is most likely to reduce data processing delays and improve GPU utilization?
- A . Switch from NVMe to traditional HDD storage for better reliability
- B . Increase the number of NVMe storage devices
- C . Optimize the data pipeline with DALI to reduce preprocessing latency
- D . Disable RAPIDS and use a CPU-based data processing approach
You are responsible for managing an AI data center that handles large-scale deep learning workloads. The performance of your training jobs has recently degraded, and you’ve noticed that the GPUs are underutilized while CPU usage remains high.
Which of the following actions would most likely resolve this issue?
- A . Reduce the batch size during training.
- B . Increase the GPU memory allocation.
- C . Optimize the data pipeline for better I/O throughput.
- D . Add more GPUs to the system.
Which of the following is a primary challenge when integrating AI into existing IT infrastructure?
- A . Scalability of the AI workloads.
- B . Ensuring AI models have a user-friendly interface.
- C . Finding AI tools that are compatible with existing hardware.
- D . Selecting the right cloud service provider.
You are managing an AI infrastructure where multiple AI workloads are being run in parallel, including image recognition, natural language processing (NLP), and reinforcement learning. Due to limited resources, you need to prioritize these workloads.
Which AI workload should you prioritize first to ensure the best overall system performance and resource allocation?
- A . Reinforcement learning
- B . Image recognition
- C . Natural Language Processing (NLP)
- D . Background data preprocessing
You are part of a team working on optimizing an AI model that processes video data in real-time. The model is deployed on a system with multiple NVIDIA GPUs, and the inference speed is not meeting the required thresholds. You have been tasked with analyzing the data processing pipeline under the guidance of a senior engineer.
Which action would most likely improve the inference speed of the model on the NVIDIA GPUs?
- A . Disable GPU power-saving features.
- B . Increase the batch size used during inference.
- C . Enable CUDA Unified Memory for the model.
- D . Profile the data loading process to ensure it’s not a bottleneck.
Your team is tasked with analyzing a large dataset to extract meaningful insights that can be used to improve the performance of your AI models. The dataset contains millions of records from various sources, and you need to apply data mining techniques to uncover patterns and trends.
Which of the following data mining techniques would be most effective for discovering patterns in large datasets used in AI workloads? (Select two)
- A . Overfitting the model to ensure it captures all possible patterns.
- B . Using a flat file to store the entire dataset.
- C . K-means clustering to group similar data points.
- D . Principal Component Analysis (PCA) to reduce the dimensionality of the dataset.
- E . Applying dropout to prevent the model from memorizing patterns.
Which of the following NVIDIA compute platforms is best suited for deploying AI workloads at the edge with minimal latency?
- A . NVIDIA Jetson
- B . NVIDIA Tesla
- C . NVIDIA RTX
- D . NVIDIA GRID
What has been the most significant factor contributing to the recent rapid improvements and widespread adoption of AI technologies across various industries?
- A . The advancement of algorithms, such as deep learning and reinforcement learning.
- B . The rise of cloud computing services providing accessible AI infrastructure.
- C . The development of more powerful and specialized AI hardware, such as NVIDIA GPUs and TPUs.
- D . The increasing availability of large datasets for training AI models.