Practice Free NCA-AIIO Exam Online Questions
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?
- A . Cloud-based CPU instances with external SSD storage
- B . Edge devices with ARM processors and distributed storage
- C . Single GPU server with local storage and manual updates
- D . Multi-GPU servers with high-speed interconnects and Kubernetes for orchestration
A company is deploying a large-scale AI training workload that requires distributed computing across multiple GPUs. They need to ensure efficient communication between GPUs on different nodes and optimize the training time.
Which of the following NVIDIA technologies should they use to achieve this?
- A . NVIDIA NVLink
- B . NVIDIA NCCL (NVIDIA Collective Communication Library)
- C . NVIDIA DeepStream SDK
- D . NVIDIA TensorRT
You are responsible for managing an AI-driven fraud detection system that processes transactions in real time. The system is hosted on a hybrid cloud infrastructure, utilizing both on-premises and cloud-based GPU clusters. Recently, the system has been missing fraud detection alerts due to delays in processing data from on-premises servers to the cloud, causing significant financial risk to the organization.
What is the MOST effective way to reduce latency and ensure timely fraud detection across the hybrid cloud environment?
- A . Increasing the number of on-premises GPU clusters to handle the workload locally.
- B . Migrating the entire fraud detection workload to on-premises servers.
- C . Switching to a single-cloud provider to centralize all processing in the cloud.
- D . Implementing a low-latency, high-throughput direct connection between the on-premises data
center and the cloud provider.
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?
- A . Use a priority queue to assign GPUs to jobs based on their deadline, ensuring the most time-sensitive tasks are completed first.
- B . Allocate GPUs to jobs based on their compute intensity, reserving the most powerful GPUs for the most demanding jobs.
- C . Allocate all GPUs to the largest job to ensure its rapid completion, then proceed with smaller jobs.
- D . Assign jobs to GPUs based on their geographic proximity to reduce data transfer times.
A retail company wants to implement an AI-based system to predict customer behavior and personalize product recommendations across its online platform. The system needs to analyze vast amounts of customer data, including browsing history, purchase patterns, and social media interactions.
Which approach would be the most effective for achieving these goals?
- A . Using a simple linear regression model to predict customer behavior based on purchase history alone
- B . Deploying a deep learning model that uses a neural network with multiple layers for feature extraction and prediction
- C . Utilizing unsupervised learning to automatically classify customers into different categories without prior labels
- D . Implementing a rule-based AI system to generate recommendations based on predefined customer
segments
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.
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.
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?
- A . The data is too large, and the CPU is not powerful enough to handle the pre-processing.
- B . The model architecture is too simple to utilize multiple GPUs effectively.
- C . The GPUs have insufficient memory to handle the dataset, leading to slow processing.
- D . The GPUs are not properly synchronized, causing some GPUs to wait for others.
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?
- A . NVIDIA JetPack
- B . NVIDIA CUDA
- C . NVIDIA DGX A100
- D . NVIDIA RAPIDS
After deploying an AI model on an NVIDIA T4 GPU in a production environment, you notice that the inference latency is inconsistent, varying significantly during different times of the day.
Which of the following actions would most likely resolve the issue?
- A . Deploy the Model on a CPU Instead of a GPU
- B . Implement GPU Isolation for the Inference Process
- C . Increase the Number of Inference Threads
- D . Upgrade the GPU Driver