NCA-AIIO AI Infrastructure and Operations Dumps

1. Download the latest NCA-AIIO exam dumps PDF for Preparation Exam : NCA-AIIO Title : AI Infrastructure and Operations https://www.passcert.com/NCA-AIIO.html 1 / 7

2. Download the latest NCA-AIIO exam dumps PDF for Preparation 1.An enterprise is deploying a large-scale AI model for real-time image recognition. They face challenges with scalability and need to ensure high availability while minimizing latency. Which combination of NVIDIA technologies would best address these needs? A. NVIDIA CUDA and NCCL B. NVIDIA Triton Inference Server and GPUDirect RDMA C. NVIDIA DeepStream and NGC Container Registry D. NVIDIA TensorRT and NVLink Answer: D 2.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 Answer: A 3.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. Answer: C 4.You are assisting a senior data scientist in analyzing a large dataset of customer transactions to identify potential fraud. The dataset contains several hundred features, but the senior team member advises you to focus on feature selection before applying any machine learning models. Which approach should you take under their supervision to ensure that only the most relevant features are used? A. Select features randomly to reduce the number of features while maintaining diversity. B. Ignore the feature selection step and use all features in the initial model. C. Use correlation analysis to identify and remove features that are highly correlated with each other. D. Use Principal Component Analysis (PCA) to reduce the dataset to a single feature. Answer: C 5.You are evaluating the performance of two AI models on a classification task. Model A has an accuracy of 85%, while Model B has an accuracy of 88%. However, Model A's F1 score is 0.90, and Model B's F1 2 / 7

3. Download the latest NCA-AIIO exam dumps PDF for Preparation score is 0.88. Which model would you choose based on the F1 score, and why? A. Model A - The F1 score is higher, indicating better balance between precision and recall. B. Model B - The higher accuracy indicates overall better performance. C. Neither - The choice depends entirely on the specific use case. D. Model B - The F1 score is lower but accuracy is more reliable. Answer: A 6.Which NVIDIA hardware and software combination is best suited for training large-scale deep learning models in a data center environment? A. NVIDIA Jetson Nano with TensorRT for training. B. NVIDIA DGX Station with CUDA toolkit for model deployment. C. NVIDIAA100 Tensor Core GPUs with PyTorch and CUDA for model training. D. NVIDIA Quadro GPUs with RAPIDS for real-time analytics. Answer: C 7.A healthcare company is looking to adopt AI for early diagnosis of diseases through medical imaging. They need to understand why AI has become so effective recently. Which factor should they consider as most impactful in enabling AI to perform complex tasks like image recognition at scale? A. Advances in GPU technology, enabling faster processing of large datasets required for AI tasks. B. Development of new programming languages specifically for AI. C. Increased availability of medical imaging data, allowing for better machine learning model training. D. Reduction in data storage costs, allowing for more data to be collected and stored. Answer: A 8.Which of the following networking features is MOST critical when designing an AI environment to handle large-scale deep learning model training? A. Enabling network redundancy to prevent single points of failure. B. Implementing network segmentation to isolate different parts of the AI environment. C. High network throughput with low latency between compute nodes. D. Using Wi-Fi for flexibility in connecting compute nodes. Answer: C 9.Your AI data center is running multiple high-performance GPU workloads, and you notice that certain servers are being underutilized while others are consistently at full capacity, leading to inefficiencies. Which of the following strategies would be most effective in balancing the workload across your AI data center? A. Implement NVIDIA GPU Operator with Kubernetes for Automatic Resource Scheduling B. Use Horizontal Scaling to Add More Servers C. Manually Reassign Workloads Based on Current Utilization D. Increase Cooling Capacity in the Data Center Answer: A 3 / 7

4. Download the latest NCA-AIIO exam dumps PDF for Preparation 10.You are tasked with deploying a machine learning model into a production environment for real-time fraud detection in financial transactions. The model needs to continuously learn from new data and adapt to emerging patterns of fraudulent behavior. Which of the following approaches should you implement to ensure the model's accuracy and relevance over time? A. Continuously retrain the model using a streaming data pipeline B. Run the model in parallel with rule-based systems to ensure redundancy C. Deploy the model once and retrain it only when accuracy drops significantly D. Use a static dataset to retrain the model periodically Answer: A 11.Your AI team is deploying a large-scale inference service that must process real-time data 24/7. Given the high availability requirements and the need to minimize energy consumption, which approach would best balance these objectives? A. Schedule inference tasks to run in batches during off-peak hours. B. Implement an auto-scaling group of GPUs that adjusts the number of active GPUs based on the real-time load. C. Use a GPU cluster with a fixed number of GPUs always running at 50% capacity to save energy. D. Use a single powerful GPU that operates continuously at full capacity to handle all inference tasks. Answer: B 12.Your team is running an AI inference workload on a Kubernetes cluster with multiple NVIDIA GPUs. You observe that some nodes with GPUs are underutilized, while others are overloaded, leading to inconsistent inference performance across the cluster. Which strategy would most effectively balance the GPU workload across the Kubernetes cluster? A. Deploying a GPU-aware scheduler in Kubernetes. B. Reducing the number of GPU nodes in the cluster. C. Implementing GPU resource quotas to limit GPU usage per pod. D. Using CPU-based autoscaling to balance the workload. Answer: A 13.Your company is developing an AI application that requires seamless integration of data processing, model training, and deployment in a cloud-based environment. The application must support real-time inference and monitoring of model performance. Which combination of NVIDIA software components is best suited for this end-to-end AI development and deployment process? A. NVIDIA RAPIDS + NVIDIA Triton Inference Server + NVIDIA DeepOps B. NVIDIA Clara Deploy SDK + NVIDIA Triton Inference Server C. NVIDIA RAPIDS + NVIDIA TensorRT D. NVIDIA DeepOps + NVIDIA RAPIDS Answer: A 14.You are assisting a senior researcher in analyzing the results of several AI model experiments conducted with different training datasets and hyperparameter configurations. The goal is to understand 4 / 7

5. Download the latest NCA-AIIO exam dumps PDF for Preparation how these variables influence model overfitting and generalization. Which method would best help in identifying trends and relationships between dataset characteristics, hyperparameters, and the risk of overfitting? A. Perform a time series analysis of accuracy across different epochs. B. Conduct a decision tree analysis to explore how dataset characteristics and hyperparameters influence overfitting. C. Create a scatter plot comparing training accuracy and validation accuracy. D. Use a histogram to display the frequency of overfitting occurrences across datasets. Answer: B 15.In a large-scale AI training environment, a data scientist needs to schedule multiple AI model training jobs with varying dependencies and priorities. Which orchestration strategy would be most effective to ensure optimal resource utilization and job execution order? A. Round-Robin Scheduling B. FIFO (First-In-First-Out) Queue C. DAG-Based Workflow Orchestration D. Manual Scheduling Answer: C 16.You are assisting a senior data scientist in optimizing a distributed training pipeline for a deep learning model. The model is being trained across multiple NVIDIA GPUs, but the training process is slower than expected. Your task is to analyze the data pipeline and identify potential bottlenecks. Which of the following is the most likely cause of the slower-than-expected training performance? A. The batch size is set too high for the GPUs' memory capacity. B. The model's architecture is too complex. C. The learning rate is too low. D. The data is not being sharded across GPUs properly. Answer: D 17.You are responsible for managing an AI infrastructure where multiple data scientists are simultaneously running large-scale training jobs on a shared GPU cluster. One data scientist reports that their training job is running much slower than expected, despite being allocated sufficient GPU resources. Upon investigation, you notice that the storage I/O on the system is consistently high. What is the most likely cause of the slow performance in the data scientist's training job? A. Insufficient GPU memory allocation B. Inefficient data loading from storage C. Incorrect CUDA version installed D. Overcommitted CPU resources Answer: B 18.Your AI team is using Kubernetes to orchestrate a cluster of NVIDIA GPUs for deep learning training jobs. Occasionally, some high-priority jobs experience delays because lower-priority jobs are consuming 5 / 7

6. Download the latest NCA-AIIO exam dumps PDF for Preparation GPU resources. Which of the following actions would most effectively ensure that high-priority jobs are allocated GPU resources first? A. Increase the Number of GPUs in the Cluster B. Configure Kubernetes Pod Priority and Preemption C. Manually Assign GPUs to High-Priority Jobs D. Use Kubernetes Node Affinity to Bind Jobs to Specific Nodes Answer: B 19.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 Answer: A, B 20.You are managing an AI cluster where multiple jobs with varying resource demands are scheduled. Some jobs require exclusive GPU access, while others can share GPUs. Which of the following job scheduling strategies would best optimize GPU resource utilization across the cluster? A. Increase the Default Pod Resource Requests in Kubernetes B. Schedule All Jobs with Dedicated GPU Resources C. Use FIFO (First In, First Out) Scheduling D. Enable GPU Sharing and Use NVIDIA GPU Operator with Kubernetes Answer: D 21.In your AI data center, you need to ensure continuous performance and reliability across all operations. Which two strategies are most critical for effective monitoring? (Select two) A. Implementing predictive maintenance based on historical hardware performance data B. Using manual logs to track system performance daily C. Conducting weekly performance reviews without real-time monitoring D. Disabling non-essential monitoring to reduce system overhead E. Deploying a comprehensive monitoring system that includes real-time metrics on CPU, GPU, memory, and network usage Answer: A, E 22.A tech startup is building a high-performance AI application that requires processing large datasets and performing complex matrix operations. The team is debating whether to use GPUs or CPUs to achieve the best performance. What is the most compelling reason to choose GPUs over CPUs for this specific use case? 6 / 7

7. Download the latest NCA-AIIO exam dumps PDF for Preparation A. GPUs have larger memory caches than CPUs, which speeds up data retrieval for AI processing. B. GPUs consume less power than CPUs, making them more energy-efficient for AI tasks. C. GPUs excel at parallel processing, which is ideal for handling large datasets and performing complex matrix operations efficiently. D. GPUs have higher single-thread performance, which is crucial for AI tasks. Answer: C 23.Which NVIDIA solution is specifically designed to accelerate data analytics and machine learning workloads, allowing data scientists to build and deploy models at scale using GPUs? A. NVIDIA JetPack B. NVIDIA CUDA C. NVIDIA DGX A100 D. NVIDIA RAPIDS Answer: D 24.You are responsible for optimizing the energy efficiency of an AI data center that handles both training and inference workloads. Recently, you have noticed that energy costs are rising, particularly during peak hours, but performance requirements are not being met. Which approach would best optimize energy usage while maintaining performance levels? A. Use liquid cooling to lower the temperature of GPUs and reduce their energy consumption. B. Implement a workload scheduling system that shifts non-urgent training jobs to off-peak hours. C. Lower the power limit on all GPUs to reduce their maximum energy consumption during all operations. D. Transition all workloads to CPUs during peak hours to reduce GPU power consumption. Answer: B 25.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. Answer: B 7 / 7