Practice Free NCA-AIIO Exam Online Questions
You are tasked with deploying an AI model that requires access to GPUs in a data center. The model is resource-intensive and needs to be scaled across multiple nodes. The data center utilizes Kubernetes for container orchestration, and the environment must maintain high availability and efficient resource utilization.
Which approach would best meet the requirements for deploying the AI model in this environment?
- A . Leverage Kubernetes with GPU-accelerated nodes and use node affinity to ensure proper GPU allocation.
- B . Use Kubernetes with CPU-only nodes to conserve GPU resources.
- C . Use Docker Swarm to manage containerized workloads.
- D . Deploy the AI model on individual VMs without containerization.
While conducting exploratory data analysis (EDA) under the guidance of a senior data scientist, you discover that some features have a significant amount of missing values. The senior team member advises you to handle this issue carefully before proceeding.
Which strategy should you use to deal with the missing data under their supervision?
- A . Impute missing values with the mean of the respective feature to maintain dataset size.
- B . Ignore the missing values, as they do not affect most machine learning algorithms.
- C . Remove all rows with any missing data to ensure only complete data is analyzed.
- D . Use a predictive model to estimate the missing values, ensuring the integrity of the dataset.
Which of the following statements correctly highlights a key difference between GPU and CPU architectures?
- A . GPUs typically have higher clock speeds than CPUs, allowing them to process individual tasks faster.
- B . CPUs are optimized for parallel processing, making them better for AI workloads, while GPUs are designed for general-purpose tasks.
- C . CPUs are specialized for graphical computations, whereas GPUs handle general-purpose computing tasks.
- D . GPUs are optimized for parallel processing, with thousands of smaller cores, while CPUs have
fewer, more powerful cores optimized for sequential processing.
Which solution should be recommended to support real-time collaboration and rendering among a team?
- A . A cluster of servers with NVIDIA T4 GPUs in each server.
- B . A DGX SuperPOD.
- C . An NVIDIA Certified Server with RTX-based GPUs.
C
Explanation:
An NVIDIA Certified Server with RTX GPUs is optimized for real-time collaboration and rendering, supporting NVIDIA Virtual Workstation (vWS) software. This setup enables low-latency, multi-user graphics workloads, ideal for team-based design or visualization. T4 GPUs focus on inference efficiency, and DGX SuperPOD targets large-scale AI training, not collaborative rendering. (Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on GPU Selection for Collaboration)
In a scenario where you need to deploy an AI workload on a virtualized infrastructure with GPU acceleration, which of the following are key considerations to ensure optimal performance? (Select two)
- A . Shared GPU Resources
- B . GPU Passthrough
- C . Storage Type (HDD vs SSD)
- D . Oversubscription of CPU
- E . Memory Overcommitment
Your team is deploying an AI model that involves a real-time recommendation system for a high-traffic e-commerce platform. The model must analyze user behavior and suggest products instantly as the user interacts with the platform.
Which type of AI workload best describes this use case?
- A . Streaming analytics
- B . Batch processing
- C . Reinforcement learning
- D . Offline training
You have completed a data mining project and have discovered several key insights from a large and complex dataset. You now need to present these insights to stakeholders in a way that clearly communicates the findings and supports data-driven decision-making.
Which of the following approaches would be most effective for visualizing insights from large datasets to support decision-making in AI projects? (Select two)
- A . Present a simple line chart showing one aspect of the data.
- B . Use a heatmap to represent correlations between variables.
- C . Generate a detailed text report with all the raw data.
- D . Visualize all data in a single pie chart.
- E . Create interactive dashboards using tools like Tableau or Power BI.
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
Your organization is building a hybrid cloud system that needs to handle a variety of tasks, including complex scientific simulations, database management, and training large AI models. You need to allocate resources effectively.
How do GPU and CPU architectures compare in terms of handling these different tasks?
- A . GPUs are better for parallel tasks like AI model training and simulations, while CPUs are better for tasks involving complex logic and sequential operations.
- B . GPUs are superior for all types of workloads in this scenario.
- C . CPUs should be used for training AI models, while GPUs are better for database management.
- D . GPUs should be used exclusively for scientific simulations, and CPUs for everything else.
Which phase of deep learning benefits the greatest from a multi-node architecture?
- A . Data Augmentation
- B . Training
- C . Inference
B
Explanation:
Training is the deep learning phase that benefits most from a multi-node architecture. It involves compute-intensive operations―forward and backward passes, gradient computation, and synchronization―across large datasets and complex models. Distributing these tasks across multiple nodes with GPUs accelerates processing, reduces time to convergence, and enables handling models too large for a single node. While data augmentation and inference can leverage multiple nodes, their gains are less pronounced, as they typically involve lighter or more localized computation. (Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Multi-Node Training)