Discover the key technical challenges of Generative AI, from large compute infrastructure to data collection and expertise. Prepare for the NVIDIA Generative AI Explained certification exam with comprehensive insights.
Table of Contents
Question
What are the technical challenges of Generative Artificial Intelligence?
A. Developing Generative AI models with a lot of parameters involves large compute infrastructure
B. Building applications that use Generative AI demands fast output generation in order to be useful
C. Training Generative AI models is compute intensive and leads to high power consumption
D. Collecting training data has complicated challenges
E. Leveraging Generative AI technology to solve problems requires expertise
Answer
A. Developing Generative AI models with a lot of parameters involves large compute infrastructure
B. Building applications that use Generative AI demands fast output generation in order to be useful
C. Training Generative AI models is compute intensive and leads to high power consumption
D. Collecting training data has complicated challenges
E. Leveraging Generative AI technology to solve problems requires expertise
Explanation
Generative AI faces numerous technical challenges that span various aspects of development and implementation:
A. Developing Generative AI models with a large number of parameters requires extensive compute infrastructure. As models become more complex and powerful, they demand significant computational resources to train and run effectively.
B. Building applications that leverage Generative AI necessitates fast output generation to ensure usability. Users expect quick responses and results, making real-time performance a critical challenge for practical applications.
C. Training Generative AI models is computationally intensive, leading to high power consumption. The energy requirements for training large models can be substantial, raising concerns about sustainability and cost.
D. Collecting training data presents complicated challenges. Generative AI models rely on vast amounts of diverse, high-quality data. Gathering, curating, and preprocessing this data can be time-consuming and resource-intensive.
E. Leveraging Generative AI technology to solve real-world problems requires specialized expertise. Developing effective solutions involves understanding the capabilities and limitations of Generative AI, as well as domain knowledge in the target application area.
Addressing these technical challenges is crucial for the successful development and deployment of Generative AI systems. NVIDIA’s Generative AI Explained certification covers these aspects, providing learners with the knowledge and skills needed to navigate the complexities of this rapidly evolving field.
NVIDIA Generative AI Explained certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the NVIDIA Generative AI Explained exam and earn NVIDIA Generative AI Explained certification.