Practice Free NCA-AIIO Exam Online Questions
Your company is building an AI-powered recommendation engine that will be integrated into an e-commerce platform. The engine will be continuously trained on user interaction data using a combination of TensorFlow, PyTorch, and XGBoost models. You need a solution that allows you to efficiently share datasets across these frameworks, ensuring compatibility and high performance on NVIDIA GPUs.
Which NVIDIA software tool would be most effective in this situation?
- A . NVIDIA TensorRT
- B . NVIDIA Nsight Compute
- C . NVIDIA CuDNN
- D . NVIDIA DALI (Data Loading Library)
You are responsible for managing an AI infrastructure that includes multiple GPU clusters for deep learning workloads. One of your tasks is to efficiently allocate resources and manage workloads across these clusters using an orchestration platform.
Which of the following approaches would best optimize the utilization of GPU resources while ensuring high availability of the AI workloads?
- A . Implement a load-balancing algorithm that dynamically assigns workloads based on real-time GPU usage and workload requirements.
- B . Use a first-come, first-served (FCFS) scheduling policy across all clusters.
- C . Use a round-robin scheduling algorithm across all GPU clusters.
- D . Assign workloads to clusters based on a predefined static schedule.
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?
- 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.
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 The GPU drivers are outdated and need updating.
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?
- A . Applying a GPU-accelerated Long Short-Term Memory (LSTM) network to the time-series data.
- B . Implementing a GPU-accelerated support vector machine (SVM) for classification.
- C . Using a simple linear regression model on a sample of the data.
- D . Visualizing the time-series data using basic line graphs to manually identify trends.
For which workloads is NVIDIA Merlin typically used?
- A . Recommender systems
- B . Natural language processing
- C . Data analytics
A
Explanation:
NVIDIA Merlin is a specialized, end-to-end framework engineered for building and deploying large-scale recommender systems. It streamlines the entire pipeline, including data preprocessing (e.g., feature engineering, data transformation), model training (using GPU-accelerated frameworks), and inference optimizations tailored for recommendation tasks. Unlike general-purpose tools for natural language processing or data analytics, Merlin is optimized to handle the unique challenges of recommendation workloads, such as processing massive user-item interaction datasets and delivering personalized results efficiently.
(Reference: NVIDIA Merlin Documentation, Overview Section)
Which of the following statements correctly differentiates between AI, Machine Learning, and Deep Learning?
- A . Machine Learning is a subset of AI, and AI is a subset of Deep Learning.
- B . AI and Deep Learning are the same, while Machine Learning is a separate concept.
- C . AI is a subset of Machine Learning, and Machine Learning is a subset of Deep Learning.
- D . Deep Learning is a subset of Machine Learning, and Machine Learning is a subset of AI.
D
Explanation:
Artificial Intelligence (AI) is the overarching field encompassing techniques to mimic human intelligence. Machine Learning (ML), a subset of AI, involves algorithms that learn from data. Deep Learning (DL), a specialized subset of ML, uses neural networks with many layers to tackle complex tasks. This hierarchical relationship―DL within ML, ML within AI―is the correct differentiation, unlike the reversed or conflated options.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on AI, ML, and DL Definitions)
Which component of the NVIDIA AI software stack is primarily responsible for optimizing deep learning inference performance by leveraging the specific architecture of NVIDIA GPUs?
- A . NVIDIA CUDA Toolkit
- B . NVIDIA TensorRT
- C . NVIDIA cuDNN
- D . NVIDIA Triton Inference Server
Your AI cluster is managed using Kubernetes with NVIDIA GPUs. Due to a sudden influx of jobs, your cluster experiences resource overcommitment, where more jobs are scheduled than the available GPU resources can handle.
Which strategy would most effectively manage this situation to maintain cluster stability?
- A . Increase the Maximum Number of Pods per Node
- B . Use Kubernetes Horizontal Pod Autoscaler Based on Memory Usage
- C . Implement Resource Quotas and LimitRanges in Kubernetes
- D . Schedule Jobs in a Round-Robin Fashion Across Nodes
Which of the following statements is true about Kubernetes orchestration?
- A . It is bare-metal based but it supports containers.
- B . It has advanced scheduling capabilities to assign jobs to available resources.
- C . It has no inferencing capabilities.
- D . It does load balancing to distribute traffic across containers.
B, D
Explanation:
Kubernetes excels in container orchestration with advanced scheduling (assigning workloads based on resource needs and availability) and load balancing (distributing traffic across pods via Services). It’s not inherently bare-metal (it runs on various platforms), and inferencing capability depends on applications, not Kubernetes itself, making B and D the true statements.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Kubernetes Orchestration)