Practice Free NCA-AIIO Exam Online Questions
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?
- 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.
You are tasked with scaling an AI inference service that processes real-time video streams from thousands of edge devices. The service is currently running on a single data center, but latency is becoming an issue due to the distance between the edge devices and the data center. You need to improve response times and ensure high availability while maintaining efficient use of resources.
Which strategy would BEST address the latency and availability challenges in this scenario?
- A . Migrating the entire workload to a cloud provider with globally distributed data centers.
- B . Deploying edge computing nodes closer to the data sources to perform initial data processing.
- C . Using compression algorithms to reduce the size of the video streams sent to the data center.
- D . Increasing the bandwidth between the data center and edge devices.
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?
- 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.
You are tasked with transforming a traditional data center into an AI-optimized data center using NVIDIA DPUs (Data Processing Units). One of your goals is to offload network and storage processing tasks from the CPU to the DPU to enhance performance and reduce latency.
Which scenario best illustrates the advantage of using DPUs in this transformation?
- A . Offloading AI model training tasks from GPUs to DPUs to free up GPU resources for inference.
- B . Using DPUs to handle network traffic encryption and decryption, freeing up CPU resources for compute-intensive AI tasks.
- C . Offloading GPU memory management tasks to DPUs to improve the efficiency of GPU-based computations.
- D . Using DPUs to process large datasets in parallel with CPUs to speed up data preprocessing for AI
workflows.
Which of the following software components is most responsible for optimizing deep learning operations on NVIDIA GPUs by providing highly tuned implementations of standard routines?
- A . NCCL
- B . cuDNN
- C . TensorFlow
- D . CUDA
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?
- A . Line chart showing average inference times per GPU.
- B . Heatmap showing inference times over time.
- C . Scatter plot of inference times versus GPU usage.
- D . Box plot for inference times across all GPUs.
A transportation company wants to implement AI to improve the safety and efficiency of its autonomous vehicle fleet. They need a solution that can handle real-time data processing, deep learning model inference, and high-throughput workloads.
Which NVIDIA solution should they consider deploying?
- A . NVIDIA Jetson
- B . NVIDIA Drive
- C . NVIDIA Clara
- D . NVIDIA DeepStream
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
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.
In a virtualized AI environment, you are responsible for managing GPU resources across several VMs running different AI workloads.
Which approach would most effectively allocate GPU resources to maximize performance and flexibility?
- A . Use GPU passthrough to allocate full GPU resources directly to one VM at a time, based on the workload’s priority.
- B . Implement GPU virtualization to allow multiple VMs to share GPU resources dynamically based on their workload requirements.
- C . Assign a dedicated GPU to each VM to ensure consistent performance for each AI workload.
- D . Deploy all AI workloads in a single VM with multiple GPUs to centralize resource management.