NCA-AIIO AI Infrastructure and Operations Exam Free Dumps

dumps2025-04-08T11:02:08+08:00

NCA-AIIO AI Infrastructure and Operations exam is easy to pass. There are 50 questions in real NCA-AIIO exam, and you have 60 minutes to answer all the questions. The available language is English. This NCA-AIIO exam is for the following audience: Business-line owners, Data center technicians, Delivery and professional services engineers, Developer operations (DevOps) engineers, IT managers, Networking engineers, Sales representatives, Systems administration staff, Solution architects, System architects. We provide the latest NCA-AIIO exam dumps, which can guarantee you success. Free AI Infrastructure and Operations NCA-AIIO exam dumps are available below.

Page 1 of 9

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?

NVIDIA CUDA and NCCL

NVIDIA Triton Inference Server and GPUDirect RDMA

NVIDIA DeepStream and NGC Container Registry

NVIDIA TensorRT and NVLink

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?

Increase the batch size

Update the CUDA version to the latest release

Disable NVLink and use PCIe for inter-GPU communication

Optimize the model's code to use mixed-precision training

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?

Monitor only the GPU utilization metrics to ensure that all GPUs are being used at full capacity.

Focus on job completion times to ensure that the most critical jobs are being finished on schedule.

Track CPU, GPU, and network utilization simultaneously to identify any resource imbalances that could lead to bottlenecks.

Use predictive analytics to forecast future GPU utilization, adjusting resources before bottlenecks occur.

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?

Select features randomly to reduce the number of features while maintaining diversity.

Ignore the feature selection step and use all features in the initial model.

Use correlation analysis to identify and remove features that are highly correlated with each other.

Use Principal Component Analysis (PCA) to reduce the dataset to a single feature.

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 score is 0.88.

Which model would you choose based on the F1 score, and why?

Model A - The F1 score is higher, indicating better balance between precision and recall.

Model B - The higher accuracy indicates overall better performance.

Neither - The choice depends entirely on the specific use case.

Model B - The F1 score is lower but accuracy is more reliable.

6. Which NVIDIA hardware and software combination is best suited for training large-scale deep learning models in a data center environment?

NVIDIA Jetson Nano with TensorRT for training.

NVIDIA DGX Station with CUDA toolkit for model deployment.

NVIDIA A100 Tensor Core GPUs with PyTorch and CUDA for model training.

NVIDIA Quadro GPUs with RAPIDS for real-time analytics.

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?

Advances in GPU technology, enabling faster processing of large datasets required for AI tasks.

Development of new programming languages specifically for A

Increased availability of medical imaging data, allowing for better machine learning model training.

Reduction in data storage costs, allowing for more data to be collected and stored.

8. Which of the following networking features is MOST critical when designing an AI environment to handle large-scale deep learning model training?

Enabling network redundancy to prevent single points of failure.

Implementing network segmentation to isolate different parts of the AI environment.

High network throughput with low latency between compute nodes.

Using Wi-Fi for flexibility in connecting compute nodes.

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?

Implement NVIDIA GPU Operator with Kubernetes for Automatic Resource Scheduling

Use Horizontal Scaling to Add More Servers

Manually Reassign Workloads Based on Current Utilization

Increase Cooling Capacity in the Data Center

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?

Continuously retrain the model using a streaming data pipeline

Run the model in parallel with rule-based systems to ensure redundancy

Deploy the model once and retrain it only when accuracy drops significantly

Use a static dataset to retrain the model periodically

Page 2 of 9

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?

Schedule inference tasks to run in batches during off-peak hours.

Implement an auto-scaling group of GPUs that adjusts the number of active GPUs based on the real-time load.

Use a GPU cluster with a fixed number of GPUs always running at 50% capacity to save energy.

Use a single powerful GPU that operates continuously at full capacity to handle all inference tasks.

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?

Deploying a GPU-aware scheduler in Kubernetes.

Reducing the number of GPU nodes in the cluster.

Implementing GPU resource quotas to limit GPU usage per pod.

Using CPU-based autoscaling to balance the workload.

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?

NVIDIA RAPIDS + NVIDIA Triton Inference Server + NVIDIA DeepOps

NVIDIA Clara Deploy SDK + NVIDIA Triton Inference Server

NVIDIA RAPIDS + NVIDIA TensorRT

NVIDIA DeepOps + NVIDIA RAPIDS

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 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?

Perform a time series analysis of accuracy across different epochs.

Conduct a decision tree analysis to explore how dataset characteristics and hyperparameters influence overfitting.

Create a scatter plot comparing training accuracy and validation accuracy.

Use a histogram to display the frequency of overfitting occurrences across datasets.

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?

Round-Robin Scheduling

FIFO (First-In-First-Out) Queue

DAG-Based Workflow Orchestration

Manual Scheduling

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?

The batch size is set too high for the GPUs' memory capacity.

The model's architecture is too complex.

The learning rate is too low.

The data is not being sharded across GPUs properly.

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?

Insufficient GPU memory allocation

Inefficient data loading from storage

Incorrect CUDA version installed

Overcommitted CPU resources

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 GPU resources.

Which of the following actions would most effectively ensure that high-priority jobs are allocated GPU resources first?

Increase the Number of GPUs in the Cluster

Configure Kubernetes Pod Priority and Preemption

Manually Assign GPUs to High-Priority Jobs

Use Kubernetes Node Affinity to Bind Jobs to Specific Nodes

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)

Memory bandwidth usage on GPUs

GPU utilization percentage

Number of active CPU threads

GPU fan noise levels

Average CPU temperature

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?

Increase the Default Pod Resource Requests in Kubernetes

Schedule All Jobs with Dedicated GPU Resources

Use FIFO (First In, First Out) Scheduling

Enable GPU Sharing and Use NVIDIA GPU Operator with Kubernetes

Page 3 of 9

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)

Implementing predictive maintenance based on historical hardware performance data

Using manual logs to track system performance daily

Conducting weekly performance reviews without real-time monitoring

Disabling non-essential monitoring to reduce system overhead

Deploying a comprehensive monitoring system that includes real-time metrics on CPU, GPU, memory, and network usage

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?

GPUs have larger memory caches than CPUs, which speeds up data retrieval for AI processing.

GPUs consume less power than CPUs, making them more energy-efficient for AI tasks.

GPUs excel at parallel processing, which is ideal for handling large datasets and performing complex matrix operations efficiently.

GPUs have higher single-thread performance, which is crucial for AI tasks.

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?

NVIDIA JetPack

NVIDIA CUDA

NVIDIA DGX A100

NVIDIA RAPIDS

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?

Use liquid cooling to lower the temperature of GPUs and reduce their energy consumption.

Implement a workload scheduling system that shifts non-urgent training jobs to off-peak hours.

Lower the power limit on all GPUs to reduce their maximum energy consumption during all operations.

Transition all workloads to CPUs during peak hours to reduce GPU power consumption.

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?

The power supply to the GPU nodes is insufficient.

The data being processed includes large datasets that are stored in GPU memory but not efficiently utilized in computation.

The workloads are being run with models that are too small for the available GPUs.

The GPU drivers are outdated and need updating.

26. You are managing an AI project for a healthcare application that processes large volumes of medical imaging data using deep learning models. The project requires high throughput and low latency during inference. The deployment environment is an on-premises data center equipped with NVIDIA GPUs. You need to select the most appropriate software stack to optimize the AI workload performance while ensuring scalability and ease of management.

Which of the following software solutions would be the best choice to deploy your deep learning models?

NVIDIA Nsight Systems

NVIDIA TensorRT

Apache MXNet

Docker

27. Which NVIDIA software component is primarily used to manage and deploy AI models in production environments, providing support for multiple frameworks and ensuring efficient inference?

NVIDIA Triton Inference Server

NVIDIA NGC Catalog

NVIDIA TensorRT

NVIDIA CUDA Toolkit

28. 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)

Keras

TensorFlow Serving

NVIDIA CUDA

NVIDIA NGC (NVIDIA GPU Cloud)

NVIDIA NCCL (NVIDIA Collective Communications Library)

29. What has been the most influential factor driving the recent rapid improvements and widespread adoption of AI technologies across various industries?

Advances in AI research methodologies, including deep learning and reinforcement learning.

The introduction of specialized AI hardware such as NVIDIA GPUs and TPUs.

The surge in global data production, providing more training data for AI models.

The increased availability of open-source AI software libraries.

30. You are responsible for scaling an AI infrastructure that processes real-time data using multiple NVIDIA GPUs. During peak usage, you notice significant delays in data processing times, even though the GPU utilization is below 80%.

What is the most likely cause of this bottleneck?

High CPU usage causing bottlenecks in data preprocessing

Inefficient data transfer between nodes in the cluster

Overprovisioning of GPU resources, leading to idle times

Insufficient memory bandwidth on the GPUs

Page 4 of 9

31. Which of the following NVIDIA compute platforms is best suited for deploying AI workloads at the edge with minimal latency?

NVIDIA Jetson

NVIDIA Tesla

NVIDIA RTX

NVIDIA GRID

32. Which industry has experienced the most profound transformation due to NVIDIA's AI infrastructure, particularly in reducing product design cycles and enabling more accurate predictive simulations?

Automotive, by accelerating the development of autonomous vehicles and enhancing safety simulations.

Retail, by improving inventory management and enhancing personalized shopping experiences.

Manufacturing, by automating quality control and improving supply chain logistics.

Finance, by enabling real-time fraud detection and improving market predictions.

33. Your team is tasked with accelerating a large-scale deep learning training job that involves processing a vast amount of data with complex matrix operations. The current setup uses high-performance CPUs, but the training time is still significant.

Which architectural feature of GPUs makes them more suitable

than CPUs for this task?

Low power consumption

Large cache memory

Massive parallelism with thousands of cores

High core clock speed

34. Which of the following is a key consideration in the design of a data center specifically optimized for AI workloads?

Prioritizing CPU core count over GPU performance in the selection of compute resources.

Optimizing network bandwidth for standard enterprise applications.

Designing the data center for maximum office space and employee facilities.

Ensuring sufficient power and cooling to support high-density GPU clusters.

35. As a junior team member, you are tasked with running data analysis on a large dataset using NVIDIA RAPIDS under the supervision of a senior engineer. The senior engineer advises you to ensure that the GPU resources are effectively utilized to speed up the data processing tasks.

What is the best approach to ensure efficient use of GPU resources during your data analysis tasks?

Focus on using only CPU cores for parallel processing

Disable GPU acceleration to avoid potential compatibility issues

Use cuDF to accelerate DataFrame operations

Use CPU-based pandas for all DataFrame operations

36. A data center is designed to support large-scale AI training and inference workloads using a combination of GPUs, DPUs, and CPUs. During peak workloads, the system begins to experience bottlenecks.

Which of the following scenarios most effectively uses GPUs and DPUs to resolve the issue?

Redistribute computational tasks from GPUs to DPUs to balance the workload evenly between both processors.

Use DPUs to take over the processing of certain AI models, allowing GPUs to focus solely on high- priority tasks.

Transfer memory management from GPUs to DPUs to reduce the load on GPUs during peak times.

Offload network, storage, and security management from the CPU to the DPU, freeing up the CPU to support the GPUs in handling AI workloads.

37. You are managing an AI infrastructure using NVIDIA GPUs to train large language models for a social media company. During training, you observe that the GPU utilization is significantly lower than expected, leading to longer training times.

Which of the following actions is most likely to improve GPU utilization and reduce training time?

Increase the batch size during training

Decrease the model complexity

Use mixed precision training

Reduce the learning rate

38. A pharmaceutical company is developing a system to predict the effectiveness of new drug compounds. The system needs to analyze vast amounts of biological data, including genomics, chemical structures, and patient outcomes, to identify promising drug candidates.

Which approach would be the most appropriate for this complex scenario?

Deploy a deep learning model with a multi-layer neural network to identify patterns in the data

Utilize reinforcement learning to continuously improve predictions based on new data from clinical trials

Use a simple linear regression model to predict drug effectiveness based on patient outcomes

Implement a rule-based AI system that uses predefined criteria to evaluate drug candidates

39. In an AI environment, the NVIDIA software stack plays a crucial role in ensuring seamless operations across different stages of the AI workflow.

Which components of the NVIDIA software stack would you use to accelerate AI model training and deployment? (Select two)

NVIDIA TensorRT

NVIDIA cuDNN (CUDA Deep Neural Network library)

NVIDIA Nsight

NVIDIA DGX-1

NVIDIA DeepStream SDK

40. When virtualizing an infrastructure that includes GPUs to support AI workloads, what is one critical factor to consider to ensure optimal performance?

Increase the number of virtual CPUs assigned to each V

Disable hyper-threading on the host machine.

Use GPU sharing technologies, like NVIDIA GRID, to allocate resources dynamically.

Assign more storage to each virtual machine.

Page 5 of 9

41. Your team is developing a predictive maintenance system for a fleet of industrial machines. The system needs to analyze sensor data from thousands of machines in real-time to predict potential failures. You have access to a high-performance AI infrastructure with NVIDIA GPUs and need to implement an approach that can handle large volumes of time-series data efficiently.

Which technique would be most appropriate for extracting insights and predicting machine failures using the available GPU resources?

Applying a GPU-accelerated Long Short-Term Memory (LSTM) network to the time-series data.

Implementing a GPU-accelerated support vector machine (SVM) for classification.

Using a simple linear regression model on a sample of the data.

Visualizing the time-series data using basic line graphs to manually identify trends.

42. A company is designing an AI-powered recommendation system that requires real-time data processing and model updates. The system should be scalable and maintain high throughput as data volume increases.

Which combination of infrastructure components and configurations is the most suitable for this scenario?

Cloud-based CPU instances with external SSD storage

Edge devices with ARM processors and distributed storage

Single GPU server with local storage and manual updates

Multi-GPU servers with high-speed interconnects and Kubernetes for orchestration

43. You are tasked with contributing to the operations of an AI data center that requires high availability and minimal downtime.

Which strategy would most effectively help maintain continuous AI operations in collaboration with the data center administrator?

Use GPUs in active-passive clusters, with DPUs handling real-time network failover and security tasks.

Deploy a redundant set of CPUs to take over GPU workloads in case of failure.

Implement a failover system where DPUs manage the AI model inference during GPU maintenance.

Schedule regular maintenance during peak hours to ensure that GPUs and DPUs are always operating at full capacity.

44. You are managing an AI-driven autonomous vehicle project that requires real-time decision-making and rapid processing of large data volumes from sensors like LiDAR, cameras, and radar. The AI models must run on the vehicle's onboard hardware to ensure low latency and high reliability.

Which NVIDIA solutions would be most appropriate to use in this scenario? (Select two)

NVIDIA Tesla T4.

NVIDIA DRIVE AGX Pegasus.

NVIDIA Jetson AGX Xavier.

NVIDIA GeForce RTX 3080.

NVIDIA DGX A100.

45. You are tasked with creating a visualization to help a senior engineer understand the distribution of inference times for an AI model deployed on multiple NVIDIA GPUs. The goal is to identify any outliers or patterns that could indicate performance issues with specific GPUs.

Which type of visualization would best help identify outliers and patterns in inference times across multiple GPUs?

Line chart showing average inference times per GP

Heatmap showing inference times over time.

Scatter plot of inference times versus GPU usage.

Box plot for inference times across all GPUs.

46. 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)

Mean Absolute Error (MAE)

Cross-Entropy Loss

F1 Score

R-squared (Coefficient of Determination)

Learning Rate

47. In your AI infrastructure, several GPUs have recently failed during intensive training sessions.

To proactively prevent such failures, which GPU metric should you monitor most closely?

Power Consumption

GPU Temperature

GPU Driver Version

Frame Buffer Utilization

48. You are working on deploying a deep learning model that requires significant GPU resources across multiple nodes. You need to ensure that the model training is scalable, with efficient data transfer between the nodes to minimize latency.

Which of the following networking technologies is most suitable for this scenario?

Fiber Channel

Ethernet (1 Gbps)

InfiniBand

Wi-Fi 6

49. In a distributed AI training environment, you notice that the GPU utilization drops significantly when the model reaches the backpropagation stage, leading to increased training time.

What is the most effective way to address this issue?

Increase the learning rate to speed up the training process.

Implement mixed-precision training to reduce the computational load during backpropagation.

Optimize the data loading pipeline to ensure continuous GPU data feeding during backpropagation.

Increase the number of layers in the model to create more work for the GPUs during backpropagation.

50. Which components are essential parts of the NVIDIA software stack in an AI environment? (Select two)

NVIDIA GameWorks

NVIDIA CUDA Toolkit

NVIDIA TensorRT

NVIDIA Nsight Systems

NVIDIA JetPack SDK

Page 6 of 9

51. You are managing the deployment of an AI-driven security system that needs to process video streams from thousands of cameras across multiple locations in real time. The system must detect potential threats and send alerts with minimal latency.

Which NVIDIA solution would be most appropriate to handle this large-scale video analytics workload?

NVIDIA RAPIDS

NVIDIA Jetson Nano

NVIDIA DeepStream

NVIDIA Clara Guardian

52. A healthcare company is using NVIDIA AI infrastructure to develop a deep learning model that can

analyze medical images and detect anomalies. The team has noticed that the model performs well during training but fails to generalize when tested on new, unseen data.

Which of the following actions is most likely to improve the model's generalization?

Use a more complex neural network architecture

Reduce the number of training epochs

Apply data augmentation techniques

Increase the batch size during training

53. 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?

Switch from NVMe to traditional HDD storage for better reliability

Increase the number of NVMe storage devices

Optimize the data pipeline with DALI to reduce preprocessing latency

Disable RAPIDS and use a CPU-based data processing approach

54. Which of the following features of GPUs is most crucial for accelerating AI workloads, specifically in the context of deep learning?

Large amount of onboard cache memory.

Lower power consumption compared to CPUs.

High clock speed.

Ability to execute parallel operations across thousands of cores.

55. A data center is running a cluster of NVIDIA GPUs to support various AI workloads. The operations team needs to monitor GPU performance to ensure workloads are running efficiently and to prevent potential hardware failures.

Which two key measures should they focus on to monitor the GPUs effectively?

(Select two)

Network bandwidth usage

Disk I/O rates

GPU temperature and power consumption

CPU clock speed

GPU memory utilization

56. Your company is deploying a real-time AI-powered video analytics application across multiple retail stores. The application requires low-latency processing of video streams, efficient GPU utilization, and the ability to scale as more stores are added. The infrastructure will use NVIDIA GPUs, and the deployment must integrate seamlessly with existing edge and cloud infrastructure.

Which combination of NVIDIA technologies would best meet the requirements for this deployment?

Deploy the application on NVIDIA DGX systems without utilizing edge devices.

Use NVIDIA RAPIDS for video processing and store processed data in a local database.

Leverage NVIDIA CUDA toolkit for development and deploy the application on generic cloud servers.

Use NVIDIA Triton Inference Server on edge devices and NVIDIA NGC for model management.

57. A telecommunications company is rolling out an AI-based system to optimize network traffic and improve customer experience across multiple regions. The system must process real-time data from millions of devices, predict network congestion, and dynamically adjust resource allocation. The infrastructure needs to ensure low latency, high availability, and the ability to scale as the network expands.

Which NVIDIA technologies would best support the deployment of this AI-based network optimization system?

Deploy the system on NVIDIA Tesla P100 GPUs with TensorFlow Serving for inference.

Implement the system using NVIDIA Jetson Xavier NX for edge computing at regional network hubs.

Use NVIDIA BlueField-2 DPUs for offloading networking tasks and NVIDIA DOCA SDK for orchestration.

Utilize NVIDIA DGX-1 with CUDA for training AI models and deploy them on CPU-based servers.

58. What is a key consideration when virtualizing accelerated infrastructure to support AI workloads on a hypervisor-based environment?

Ensure GPU passthrough is configured correctly.

Disable GPU overcommitment in the hypervisor.

Enable vCPU pinning to specific cores.

Maximize the number of VMs per physical server.

59. In your AI data center, you are responsible for deploying and managing multiple machine learning models in production. To streamline this process, you decide to implement MLOps practices with a focus on job scheduling and orchestration.

Which of the following strategies is most aligned with achieving reliable and efficient model deployment?

Schedule all jobs to run at the same time to maximize GPU utilization.

Deploy models directly to production without staging environments.

Use a CI/CD pipeline to automate model training, validation, and deployment.

Manually trigger model deployments based on performance metrics.

60. You are deploying a large-scale AI model training pipeline on a cloud-based infrastructure that uses NVIDIA GPUs. During the training, you observe that the system occasionally crashes due to memory overflows on the GPUs, even though the overall GPU memory usage is below the maximum capacity.

What is the most likely cause of the memory overflows, and what should you do to mitigate this issue?

The model's batch size is too large; reduce the batch size.

The system is encountering fragmented memory; enable unified memory management.

The GPUs are not receiving data fast enough; increase the data pipeline speed.

The CPUs are overloading the GPUs; allocate more CPU cores to handle preprocessing.

Page 7 of 9

61. You are responsible for managing an AI data center that supports various AI workloads, including training, inference, and data processing.

Which two practices are essential for ensuring optimal resource utilization and minimizing downtime? (Select two)

Regularly monitoring and updating firmware on GPUs and other hardware

Disabling alerts for non-critical issues to reduce alert fatigue

Limiting the use of virtualization to reduce overhead

Running all AI workloads during peak usage hours to maximize efficiency

Implementing automated workload scheduling based on resource availability

62. You are working on a regression task to predict car prices. Model Gamma has a Mean Absolute Error (MAE) of $1,200, while Model Delta has a Mean Absolute Error (MAE) of $1,500.

Which model should be preferred based on the Mean Absolute Error (MAE), and what does this metric indicate?

Neither model is better because MAE is not suitable for comparing regression models.

Model Delta is better because it has a higher MAE, which means it's more flexible.

Model Gamma is worse because lower MAE can indicate overfitting.

Model Gamma is better because it has a lower MA

63. 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?

Use CPUs for data analytics and GPUs for mathematical calculations.

Use GPUs for mathematical calculations and CPUs for managing I/O operations.

Use CPUs for mathematical calculations and GPUs for data analytics.

Use GPUs for both the mathematical calculations and data analytics.

64. You are managing an AI training workload that requires high availability and minimal latency. The data is stored across multiple geographically dispersed data centers, and the compute resources are provided by a mix of on-premises GPUs and cloud-based instances. The model training has been experiencing inconsistent performance, with significant fluctuations in processing time and unexpected downtime.

Which of the following strategies is MOST effective in improving the consistency and reliability of the AI training process?

Implementing a hybrid load balancer to dynamically distribute workloads across cloud and on-premises resources.

Switching to a single-cloud provider to consolidate all compute resources.

Migrating all data to a centralized data center with high-speed networking.

Upgrading to the latest version of GPU drivers on all machines.

65. You are optimizing an AI data center that uses NVIDIA GPUs for energy efficiency.

Which of the following practices would most effectively reduce energy consumption while maintaining performance?

Disabling power capping to allow full power usage

Enabling NVIDIA’s Adaptive Power Management features

Utilizing older GPUs to reduce power consumption

Running all GPUs at maximum clock speeds

66. You are helping a senior engineer analyze the results of a hyperparameter tuning process for a machine learning model. The results include a large number of trials, each with different hyperparameters and corresponding performance metrics. The engineer asks you to create visualizations that will help in understanding how different hyperparameters impact model performance.

Which type of visualization would be most appropriate for identifying the relationship between hyperparameters and model performance?

Line chart showing performance metrics over trials.

Pie chart showing the proportion of successful trials.

Parallel coordinates plot showing hyperparameters and performance metrics.

Scatter plot of hyperparameter values against performance metrics.

67. Your AI-driven data center experiences occasional GPU failures, leading to significant downtime for critical AI applications. To prevent future issues, you decide to implement a comprehensive GPU health monitoring system. You need to determine which metrics are essential for predicting and preventing GPU failures.

Which of the following metrics should be prioritized to predict potential GPU failures and maintain GPU health?

GPU Temperature

CPU Utilization

Error Rates (e.g., ECC errors)

GPU Clock Speed

68. Which of the following statements best explains why AI workloads are more effectively handled by distributed computing environments?

AI models are inherently simpler, making them well-suited to distributed environments.

Distributed computing environments allow parallel processing of AI tasks, speeding up training and inference times.

Distributed systems reduce the need for specialized hardware like GPUs.

AI workloads require less memory than traditional workloads, which is best managed by distributed systems.

69. You are managing an AI data center where energy consumption has become a critical concern due to rising costs and sustainability goals. The data center supports various AI workloads, including model training, inference, and data preprocessing.

Which strategy would most effectively reduce energy consumption without significantly impacting performance?

Schedule all AI workloads during nighttime to take advantage of lower electricity rates.

Reduce the clock speed of all GPUs to lower power consumption.

Consolidate all AI workloads onto a single GPU to reduce overall power usage.

Implement dynamic voltage and frequency scaling (DVFS) to adjust GPU power usage based on real-time workload demands.

70. You are working on an AI project that involves training multiple machine learning models to predict customer churn. After training, you need to compare these models to determine which one performs best. The models include a logistic regression model, a decision tree, and a neural network.

Which of the following loss functions and performance metrics would be most appropriate to use for comparing the performance of these models? (Select two)

Mean Squared Error (MSE) for the decision tree model.

Using the proportion of explained variance (R²) for the neural network.

F1-score for comparing model performance on an imbalanced dataset.

Cross-entropy loss for the logistic regression and neural network models.

Accuracy for all models as the sole performance metric.

Page 8 of 9

71. Your AI development team is working on a project that involves processing large datasets and training multiple deep learning models. These models need to be optimized for deployment on different hardware platforms, including GPUs, CPUs, and edge devices.

Which NVIDIA software component would best facilitate the optimization and deployment of these models across different platforms?

NVIDIA DIGITS

NVIDIA TensorRT

NVIDIA RAPIDS

NVIDIA Triton Inference Server

72. You are tasked with deploying a new AI-based video analytics system for a smart city project. The system must process real-time video streams from multiple cameras across the city, requiring low latency and high computational power. However, budget constraints limit the number of high-performance servers you can deploy.

Which of the following strategies would best optimize the deployment of this AI system? (Select two)

Disable redundant safety checks in the AI algorithms to improve processing speed.

Increase the number of cameras to capture more data for analysis.

Use older, less expensive GPUs to save on hardware costs.

Implement a hybrid cloud solution, combining local servers with cloud resources.

Utilize edge computing to process data closer to the cameras.

73. An autonomous vehicle company is developing a self-driving car that must detect and classify objects such as pedestrians, other vehicles, and traffic signs in real-time. The system needs to make split-second decisions based on complex visual data.

Which approach should the company prioritize to effectively address this challenge?

Develop an unsupervised learning algorithm to cluster visual data and classify objects based on their proximity

Apply a linear regression model to predict the position of objects based on camera inputs

Implement a deep learning model with convolutional neural networks (CNNs) to process and classify the visual data

Use a rule-based AI system to classify objects based on predefined visual characteristics

74. 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?

Disable GPU power-saving features.

Increase the batch size used during inference.

Enable CUDA Unified Memory for the model.

Profile the data loading process to ensure it’s not a bottleneck.

75. When virtualizing a GPU-accelerated infrastructure, which of the following is a critical consideration to ensure optimal performance for AI workloads?

Ensuring proper NUMA (Non-Uniform Memory Access) alignment

Using software-based GPU virtualization instead of hardware passthrough

Maximizing the number of VMs per GPU

Allocating more virtual CPUs (vCPUs) than physical CPUs

76. You are working with a team of data scientists on an AI project where multiple machine learning models are being trained to predict customer churn. The models are evaluated based on the Mean Squared Error (MSE) as the loss function. However, one model consistently shows a higher MSE despite having a more complex architecture compared to simpler models.

What is the most likely reason for the higher MSE in the more complex model?

Low learning rate in model training

Overfitting to the training data

Incorrect calculation of the loss function

Underfitting due to insufficient model complexity

77. Your company is planning to deploy a range of AI workloads, including training a large convolutional neural network (CNN) for image classification, running real-time video analytics, and performing batch processing of sensor data.

What type of infrastructure should be prioritized to support these diverse AI workloads effectively?

A cloud-based infrastructure with serverless computing options

On-premise servers with large storage capacity

CPU-only servers with high memory capacity

A hybrid cloud infrastructure combining on-premise servers and cloud resources

78. You are working with a team of data scientists who are training a large neural network model on a multi-node NVIDIA DGX system. They notice that the training is not scaling efficiently across the nodes, leading to underutilization of the GPUs and slower-than-expected training times.

What could be the most likely reasons for the inefficiency in training across the nodes? (Select two)

Incorrect configuration of NVIDIA CUDA cores on each node.

Incorrect implementation of model parallelism.

Lack of sufficient GPU memory on each node.

Improper use of NVIDIA NCCL (NVIDIA Collective Communications Library).

Insufficient bandwidth of the interconnect between nodes.

79. A healthcare company is training a large convolutional neural network (CNN) for medical image analysis. The dataset is enormous, and training is taking longer than expected. The team needs to speed up the training process by distributing the workload across multiple GPUs and nodes.

Which of the following NVIDIA solutions will help them achieve optimal performance?

NVIDIA DeepStream SDK

NVIDIA NCCL and NVIDIA DALI

NVIDIA TensorRT

NVIDIA cuDNN

80. 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?

The data is too large, and the CPU is not powerful enough to handle the pre-processing.

The model architecture is too simple to utilize multiple GPUs effectively.

The GPUs have insufficient memory to handle the dataset, leading to slow processing.

The GPUs are not properly synchronized, causing some GPUs to wait for others.

Page 9 of 9

81. In a complex AI-driven autonomous vehicle system, the computing infrastructure is composed of multiple GPUs, CPUs, and DPUs.

During real-time object detection, which of the following best explains how these components interact to optimize performance?

The CPU processes the object detection model, while the GPU and DPU handle data preprocessing and post-processing tasks respectively.

The GPU handles object detection algorithms, while the CPU manages the vehicle's control systems, and the DPU accelerates image preprocessing tasks.

The GPU processes object detection algorithms, the CPU handles decision-making logic, and the DPU offloads data transfer and security tasks from the CP

The GPU processes the object detection model, the DPU offloads network traffic from the GPU, and the CPU handles peripheral device management.

82. You are working on a project that involves analyzing a large dataset of satellite images to detect deforestation. The dataset is too large to be processed on a single machine, so you need to distribute the workload across multiple GPU nodes in a high-performance computing cluster. The goal is to use image segmentation techniques to accurately identify deforested areas.

Which approach would be most effective in processing this large dataset of satellite images for deforestation detection?

Manually reviewing the images and marking deforested areas for analysis.

Using a CPU-based image processing library to preprocess the images before segmentation.

Storing the images in a traditional relational database for easy access and querying.

Implementing a distributed GPU-accelerated Convolutional Neural Network (CNN) for image segmentation.

83. A financial services company is developing a machine learning model to detect fraudulent transactions in real-time. They need to manage the entire AI lifecycle, from data preprocessing to model deployment and monitoring.

Which combination of NVIDIA software components should they integrate to ensure an efficient and scalable AI development and deployment process?

NVIDIA Metropolis for data collection, DIGITS for training, and Triton Inference Server for deployment.

NVIDIA Clara for model training, TensorRT for data processing, and Jetson for deployment.

NVIDIA DeepStream for data processing, CUDA for model training, and NGC for deployment.

NVIDIA RAPIDS for data processing, TensorRT for model optimization, and Triton Inference Server for deployment.

84. In an effort to optimize your data center for AI workloads, you deploy NVIDIA DPUs to offload network and security tasks from CPUs. Despite this, your AI applications still experience high latency during peak processing times.

What is the most likely cause of the latency, and how can it be addressed?

The DPUs are not optimized for AI inference, causing delays in processing tasks that should remain on the CPU or GP

The DPUs are offloading too many tasks, leading to underutilization of the CPUs and causing latency.

The network infrastructure is outdated, limiting the effectiveness of the DPUs in reducing latency.

The AI workloads are too large for the DPUs to handle, causing them to slow down other operations.

85. Which of the following best describes how memory and storage requirements differ between training and inference in AI systems?

Training and inference have identical memory and storage requirements since both involve processing similar data

Inference usually requires more memory than training because of the need to load multiple models simultaneously

Training generally requires more memory and storage due to the need to process large datasets and maintain model states

Training can be done with minimal memory, focusing more on GPU performance, while inference needs high memory for rapid processing

86. In your AI data center, you’ve observed that some GPUs are underutilized while others are frequently maxed out, leading to uneven performance across workloads.

Which monitoring tool or technique would be most effective in identifying and resolving these GPU utilization imbalances?

Use NVIDIA DCGM to Monitor and Report GPU Utilization

Perform Manual Daily Checks of GPU Temperatures

Set Up Alerts for Disk I/O Performance Issues

Monitor CPU Utilization Using Standard System Monitoring Tools

87. You are responsible for managing an AI infrastructure that runs a critical deep learning application. The application experiences intermittent performance drops, especially when processing large datasets. Upon investigation, you find that some of the GPUs are not being fully utilized while others are overloaded, causing the overall system to underperform.

What would be the most effective solution to address the uneven GPU utilization and optimize the performance of the deep learning application?

Reduce the size of the datasets being processed.

Increase the clock speed of the GPUs.

Implement dynamic load balancing for the GPUs.

Add more GPUs to the system.

88. You are managing a high-performance AI cluster where multiple deep learning jobs are scheduled to run concurrently.

To maximize resource efficiency, which of the following strategies should you use to allocate GPU resources across the cluster?

Use a priority queue to assign GPUs to jobs based on their deadline, ensuring the most time-sensitive tasks are completed first.

Allocate GPUs to jobs based on their compute intensity, reserving the most powerful GPUs for the most demanding jobs.

Allocate all GPUs to the largest job to ensure its rapid completion, then proceed with smaller jobs.

Assign jobs to GPUs based on their geographic proximity to reduce data transfer times.

89. Your AI team is working on a complex model that requires both training and inference on large datasets. You notice that the training process is extremely slow, even with powerful GPUs, due to frequent data transfer between the CPU and GPU.

Which approach would best minimize these data transfer bottlenecks and accelerate the training process?

Transfer all data to the GPU at the start of the training process and keep it there until training is complete.

Increase the batch size to reduce the number of data transfers between the CPU and GP

Utilize multiple GPUs to split the data processing across them, regardless of the data transfer issues.

Use a CPU with higher clock speed to speed up data transfer to the GP

90. A data science team compares two regression models for predicting housing prices. Model X has an R-squared value of 0.85, while Model Y has an R-squared value of 0.78. However, Model Y has a lower Mean Absolute Error (MAE) than Model X.

Based on these statistical performance metrics, which model should be chosen for deployment, and why?

Model X should be chosen because it is likely to perform better on unseen data.

Model X should be chosen because a higher R-squared value indicates it explains more variance in the data.

Model Y should be chosen because a lower MAE indicates it has better prediction accuracy.

Model X should be chosen because R-squared is a more comprehensive metric than MA

NCA-AIIO AI Infrastructure and Operations Exam Free Dumps

NCA-AIIO AI Infrastructure and Operations Exam Free Dumps

Share this post