Practice Free NCA-AIIO Exam Online Questions
In an MLOps pipeline, you are responsible for managing the training and deployment of machine learning models on a multi-node GPU cluster. The data used for training is updated frequently.
How should you design your job scheduling process to ensure models are trained on the most recent data without causing unnecessary delays in deployment?
- A . Schedule the entire pipeline to run at fixed intervals, regardless of data updates.
- B . Use a round-robin scheduling policy across all pipeline stages, regardless of data freshness.
- C . Train models only once per week and deploy them immediately after training.
- D . Implement an event-driven scheduling system that triggers the pipeline whenever new data is
ingested.
You are working on a project that involves monitoring the performance of an AI model deployed in production. The model’s accuracy and latency metrics are being tracked over time. Your task, under the guidance of a senior engineer, is to create visualizations that help the team understand trends in these metrics and identify any potential issues.
Which visualization would be most effective for showing trends in both accuracy and latency metrics over time?
- A . Pie chart showing the distribution of accuracy metrics.
- B . Stacked area chart showing cumulative accuracy and latency.
- C . Dual-axis line chart with accuracy on one axis and latency on the other.
- D . Box plot comparing accuracy and latency.
Which two software components are directly involved in the life cycle of AI development and deployment, particularly in model training and model serving? (Select two)
- A . Kubeflow
- B . MLflow
- C . Apache Spark
- D . Prometheus
- E . Airflow
Which of the following factors has most significantly contributed to the recent rapid improvements and widespread adoption of AI?
- A . The invention of new AI programming languages.
- B . The global standardization of AI ethics guidelines.
- C . Increased computational power, especially with the advent of modern GPUs and specialized AI hardware.
- D . The rise of social media, increasing the need for AI.
In an effort to improve energy efficiency in your AI infrastructure using NVIDIA GPUs, you’re considering several strategies.
Which of the following would most effectively balance energy efficiency with maintaining performance?
- A . Disabling all energy-saving features to ensure maximum performance
- B . Employing NVIDIA GPU Boost technology to dynamically adjust clock speeds
- C . Running all GPUs at the lowest possible clock speeds
- D . Enabling deep sleep mode on all GPUs during processing times
You are tasked with virtualizing the GPU resources in a multi-tenant AI infrastructure where different teams need isolated access to GPU resources.
Which approach is most suitable for ensuring efficient resource sharing while maintaining isolation between tenants?
- A . NVIDIA vGPU (Virtual GPU) Technology
- B . Deploying Containers Without GPU Isolation
- C . Implementing CPU-based Virtualization
- D . Using GPU Passthrough for Each Tenant
You are responsible for setting up the AI infrastructure for a new AI-driven video analytics platform. The platform will use multiple AI frameworks, including TensorFlow, PyTorch, and MXNet. The development team requires an environment where they can quickly switch between these frameworks for model training, and you need to ensure efficient resource utilization on NVIDIA GPUs across the different frameworks.
Which NVIDIA software component is best suited to meet these requirements?
- A . NVIDIA DeepStream SDK
- B . NVIDIA TensorFlow Container
- C . NVIDIA NGC
- D . Kubernetes
You are responsible for managing an AI data center that handles large-scale deep learning workloads. The performance of your training jobs has recently degraded, and you’ve noticed that the GPUs are underutilized while CPU usage remains high.
Which of the following actions would most likely resolve this issue?
- A . Reduce the batch size during training.
- B . Increase the GPU memory allocation.
- C . Optimize the data pipeline for better I/O throughput.
- D . Add more GPUs to the system.
You are assisting a senior data scientist in analyzing a large dataset of customer transactions to identify potential fraud. The dataset contains several hundred features, but the senior team member advises you to focus on feature selection before applying any machine learning models.
Which approach should you take under their supervision to ensure that only the most relevant features are used?
- A . Select features randomly to reduce the number of features while maintaining diversity.
- B . Ignore the feature selection step and use all features in the initial model.
- C . Use correlation analysis to identify and remove features that are highly correlated with each other.
- D . Use Principal Component Analysis (PCA) to reduce the dataset to a single feature.
A senior data scientist requests that the results of a deep learning model’s performance be visualized using GPU-accelerated software. The goal is to efficiently render large-scale data visualizations that can demonstrate trends and patterns over time.
Which two methods should be employed to achieve this? (Select two)
- A . Employ NVIDIA Omniverse for rendering 3D visualizations.
- B . Use RAPIDS cuGraph for network graph visualizations.
- C . Generate visualizations using Tableau with default CPU processing.
- D . Utilize ggplot2 in R for advanced statistical visualizations.
- E . Rely on Power BI to create dashboards without leveraging GPU capabilities.