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EMC Dell GenAI Foundations Achievement Sample Questions (Q19-Q24):
NEW QUESTION # 19
What is the purpose of fine-tuning in the generative Al lifecycle?
Answer: C
Explanation:
Customization: Fine-tuning involves adjusting a pretrained model on a smaller dataset relevant to a specific task, enhancing its performance for that particular application.
NEW QUESTION # 20
What is the difference between supervised and unsupervised learning in the context of training Large Language Models (LLMs)?
Answer: C
Explanation:
Supervised Learning: Involves using labeled datasets where the input-output pairs are provided. The AI system learns to map inputs to the correct outputs by minimizing the error between its predictions and the actual labels.
NEW QUESTION # 21
What impact does bias have in Al training data?
Answer: B
Explanation:
Definition of Bias: Bias in AI refers to systematic errors that can occur in the model due to prejudiced assumptions made during the data collection, model training, or deployment stages.
NEW QUESTION # 22
Whatis the role of a decoder in a GPT model?
Answer: C
Explanation:
In the context of GPT (Generative Pre-trained Transformer) models, the decoder plays a crucial role. Here's a detailed explanation:
Decoder Function:The decoder in a GPT model is responsible for taking the input (often a sequence of text) and generating the appropriate output (such as a continuation of the text or an answer to a query).
Architecture:GPT models are based on the transformer architecture, where the decoder consists of multiple layers of self-attention and feed-forward neural networks.
Self-Attention Mechanism:This mechanism allows the model to weigh the importance of different words in the input sequence, enabling it to generate coherent and contextually relevant output.
Generation Process:During generation, the decoder processes the input through these layers to produce the next word in the sequence, iteratively constructing the complete output.
References:
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I.
(2017). Attention is All You Need. In Advances in Neural Information Processing Systems.
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. OpenAI Blog.
NEW QUESTION # 23
A machine learning engineer is working on a project that involves training a model using labeled data.
What type of learning is he using?
Answer: C
Explanation:
When a machine learning engineer is training a model using labeled data, the type of learning being employed is supervised learning. In supervised learning, the model is trained on a labeled dataset, which means that each training example is paired with an output label. The model learns to predict the output from the input data, and the goal is to minimize the difference between the predicted and actual outputs.
The Official Dell GenAI Foundations Achievement document likely covers the fundamental concepts of machine learning, including supervised learning, as it is one of the primary categories of machine learning. It would explain that supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs12. The data is known as training data, and it consists of a set of training examples. Each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). The supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.
Self-supervised learning (Option OA) is a type of unsupervised learning where the system learns to predict part of its input from other parts. Unsupervised learning (Option OB) involves training a model on data that does not have labeled responses. Reinforcement learning (Option OD) is a type of learning where an agent learns to make decisions by performing actions and receiving rewards or penalties. Therefore, the correct answer is C. Supervised learning, as it directly involves the use of labeled data for training models.
NEW QUESTION # 24
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