EMC D-GAI-F-01 Dumps

When a data scientist is customizing a pre-trained language model (LLM) to perform a specific task, she is in the fine-tuning phase of the LLM lifecycle. Fine-tuning is a process where a pre-trained model is further trained (or fine-tuned) on a smaller, task-specific dataset. This allows the model to adapt to the nuances and specific requirements of the task at hand.

The lifecycle of an LLM typically involves several stages:

Pre-training: The model is trained on a large, general dataset to learn a wide range of language patterns and knowledge.

Fine-tuning: After pre-training, the model is fine-tuned on a specific dataset related to the task it needs to perform.

Inferencing: This is the stage where the model is deployed and used to make predictions or generate text based on new input data.

The data collection phase (Option OB) would precede pre-training, and it involves gathering the large datasets necessary for the initial training of the model. Training (Option OC) is a more general term that could refer to either pre-training or fine-tuning, but in the context of customization for a specific task, fine-tuning is the precise term. Inferencing (Option OA) is the phase where the model is actually used to perform the task it was trained for, which comes after fine-tuning.

Therefore, the correct answer is D. Fine-tuning, as it is the phase focused on customizing and adapting the pre-trained model to the specific task12345.

Governance in Generative AI involves several key aspects, among which transparency is crucial. Transparency in AI governance refers to the clarity and openness regarding how AI systems operate, the data they use, the decision-making processes they employ, and the way they are developed and deployed. It ensures that stakeholders understand AI processes and can trust the outcomes produced by AI systems.

The Official Dell GenAI Foundations Achievement document likely emphasizes the importance of transparency as part of ethical AI governance. It would discuss the need for clear communication about AI operations to build trust and ensure accountability1. Additionally, transparency is a foundational element in addressing ethical considerations, reducing bias, and ensuring that AI systems are used responsibly2.

User interface design (Option OB), speed of deployment (Option OC), and cost efficiency (Option OD) are important factors in the development and implementation of AI systems but are not specifically governance aspects. Governance focuses on the overarching principles and practices that guide the ethical and responsible use of AI, making transparency the key aspect in this context.

Diversity in AI training data is crucial for developing robust and fair AI models. The correct answer is option C. Here's why:

Generalization: A diverse training dataset ensures that the AI model can generalize well across different scenarios and perform accurately in real-world applications.

Bias Reduction: Diverse data helps in mitigating biases that can arise from over-representation or under-representation of certain groups or scenarios.

Fairness and Inclusivity: Ensuring diversity in data helps in creating AI systems that are fair and inclusive, which is essential for ethical AI development.

References:

Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning. fairmlbook.org.

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys (CSUR), 54(6), 1-35.