Practice Free NCA-AIIO Exam Online Questions
A research team is deploying a deep learning model on an NVIDIA DGX A100 system. The model has high computational demands and requires efficient use of all available GPUs. During the deployment, they notice that the GPUs are underutilized, and the inter-GPU communication seems to be a bottleneck. The software stack includes TensorFlow, CUDA, NCCL, and cuDNN.
Which of the following actions would most likely optimize the inter-GPU communication and improve overall GPU utilization?
- A . Ensure NCCL is configured correctly for optimal bandwidth utilization
- B . Disable cuDNN to streamline GPU operations
- C . Switch to using a single GPU to reduce complexity
- D . Increase the number of data parallel jobs running simultaneously
Which aspect of computing uses large amounts of data to train complex neural networks?
- A . Machine learning
- B . Deep learning
- C . Inferencing
B
Explanation:
Deep learning, a subset of machine learning, relies on large datasets to train multi-layered neural networks, enabling them to learn hierarchical feature representations and complex patterns autonomously. While machine learning encompasses broader techniques (some requiring less data), deep learning’s dependence on vast data volumes distinguishes it. Inferencing, the application of trained models, typically uses smaller, real-time inputs rather than extensive training data. (Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Deep Learning Fundamentals)
You are assisting a senior data scientist in a project aimed at improving the efficiency of a deep learning model. The team is analyzing how different data preprocessing techniques impact the model’s accuracy and training time. Your task is to identify which preprocessing techniques have the most significant effect on these metrics.
Which method would be most effective in identifying the preprocessing techniques that significantly affect model accuracy and training time?
- A . Conduct a t-test between different preprocessing techniques.
- B . Use a line chart to plot training time for different preprocessing techniques.
- C . Perform a multivariate regression analysis with preprocessing techniques as independent variables.
- D . Create a pie chart showing the distribution of preprocessing techniques used.
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?
- A . NVIDIA Triton Inference Server
- B . NVIDIA NGC Catalog
- C . NVIDIA TensorRT
- D . NVIDIA CUDA Toolkit
You are deploying a machine learning pipeline that includes data preprocessing, model training, and model deployment. Your infrastructure supports both on-demand and scheduled job execution.
How should you configure your MLOps pipeline to ensure that the model is trained and deployed with the most recent data without overloading the system?
- A . Use event-driven triggers to start the pipeline when new data is available.
- B . Schedule all jobs to run every hour, regardless of data updates.
- C . Schedule jobs to run during off-peak hours only, without considering data freshness.
- D . Run the entire pipeline manually whenever new data is available.
Your AI data center supports a variety of deep learning applications, and ensuring continuous operation is critical. The data center uses NVIDIA GPUs, and your task is to monitor GPU health and performance to prevent downtime. You have several monitoring tools and techniques available.
Which of the following monitoring strategies is most effective for predicting and preventing GPU failures in an AI data center?
- A . Regular manual inspection of GPU performance data
- B . Monitoring network traffic between GPUs and storage to identify bottlenecks
- C . Relying solely on temperature thresholds to detect potential GPU issues
- D . Integration of an AI-based predictive maintenance system that analyzes GPU telemetry data in real-
time
Which of the following best describes how memory and storage requirements differ between training and inference in AI systems?
- A . Training and inference have identical memory and storage requirements since both involve processing similar data
- B . Inference usually requires more memory than training because of the need to load multiple models simultaneously
- C . Training generally requires more memory and storage due to the need to process large datasets and maintain model states
- D . Training can be done with minimal memory, focusing more on GPU performance, while inference
needs high memory for rapid processing
What factors have led to significant breakthroughs in Deep Learning?
- A . Advances in hardware, availability of fast internet connections, and improvements in training algorithms.
- B . Advances in sensors, availability of large datasets, and improvements to the “Bag of Words” algorithm.
- C . Advances in hardware, availability of large datasets, and improvements in training algorithms.
- D . Advances in smartphones, social media sites, and improvements in statistical techniques.
C
Explanation:
Deep learning breakthroughs stem from three pillars: advances in hardware (e.g., GPUs and TPUs) providing the compute power for large-scale neural networks; the availability of large datasets offering the data volume needed for training; and improvements in training algorithms (e.g., optimizers like Adam, novel architectures like Transformers) enhancing model efficiency and accuracy. While internet speed, sensors, or smartphones play roles in broader tech, they’re less directly tied to deep learning’s core advancements.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Deep Learning Advancements)
In an AI data center, you are working with a professional administrator to optimize the deployment of AI workloads across multiple servers.
Which of the following actions would best contribute to improving the efficiency and performance of the data center?
- A . Distribute AI workloads across multiple servers with GPUs, while using DPUs to manage network traffic and storage access.
- B . Allocate all networking tasks to the CPUs, allowing the GPUs and DPUs to focus solely on AI model processing.
- C . Consolidate all AI workloads onto a single high-performance server to maximize GPU utilization.
- D . Use the CPUs exclusively for AI training tasks while GPUs and DPUs handle background
operations.
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)
- A . Incorrect configuration of NVIDIA CUDA cores on each node.
- B . Incorrect implementation of model parallelism.
- C . Lack of sufficient GPU memory on each node.
- D . Improper use of NVIDIA NCCL (NVIDIA Collective Communications Library).
- E . Insufficient bandwidth of the interconnect between nodes.