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Oracle Cloud Infrastructure 2023 AI Foundations Associate Sample Questions (Q27-Q32):NEW QUESTION # 27 What is "in-context learning" in the realm of large Language Models (LLMs)?
* A. Training a model on a diverse range of tasks
* B. Teaching a mode! through zero-shot learning
* C. Modifying the behavior of a pretrained LLM permanently
* D. Providing a few examples of a target task via the input prompt
Answer: DExplanation:
In-context learning is a technique that leverages the ability of large language models to learn from a few input-output examples provided in the input prompt. By conditioning on these examples, the model can infer the task and the format of the desired output, and generate a suitable response. In-context learning does not require any additional training or fine-tuning of the model, and can be used for various tasks such as text summarization, question answering, text generation, and more45. In-context learning is also known as few-shot learning or prompt-based learning. Reference: [2307.12375] In-Context Learning in Large Language Models Learns Label ...](https://arxiv.org/abs/2307.12375), [2307.07164] Learning to Retrieve In-Context Examples for Large Language Models](https://arxiv.org/abs/2307.07164)
NEW QUESTION # 28 How is Generative AI different from other AI approaches?
* A. Generative AI generates labeled outputs for training.
* B. Generative AI is used exclusively for text-based applications.
* C. Generative AI focuses on decision-making and optimization.
* D. Generative AI understands underlying data and creates new examples.
Answer: DExplanation:
Generative AI is a branch of artificial intelligence that focuses on creating new content or data based on the patterns and structure of existing data. Unlike other AI approaches that aim to recognize, classify, or predict data, generative AI aims to generate data that is realistic, diverse, and novel. Generative AI can produce various types of content, such as images, text, audio, video, software code, product designs, and more. Generative AI uses different techniques and models to learn from data and generate new examples, such as generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, and foundation models. Generative AI has many applications across different domains and industries, such as art, entertainment, education, healthcare, engineering, marketing, and more. Reference: : Oracle Cloud Infrastructure AI - Generative AI, Generative artificial intelligence - Wikipedia
NEW QUESTION # 29 Which type of machine learning is used for already labeled data sets?
* A. Unsupervised earning
* B. Active learning
* C. Supervised learning
* D. Reinforcement learning
Answer: CExplanation:
Supervised learning is a type of machine learning that uses labeled data sets to train algorithms that can classify data or predict outcomes. Labeled data sets are data sets that have both input features and output labels for each instance. For example, a labeled data set for image classification would have images as input features and the corresponding categories (such as dog, cat, bird, etc.) as output labels. Supervised learning algorithms learn the relationship between the input features and the output labels from the training data set and then use that relationship to make predictions on new or unseen data. Supervised learning can be divided into two subtypes: classification and regression. Classification is the task of assigning discrete categories to data instances, such as spam or not spam for emails. Regression is the task of predicting continuous values for data instances, such as house prices or stock prices. Reference: : Oracle Cloud Infrastructure AI - Machine Learning Concepts, What is Supervised Learning? | IBM
NEW QUESTION # 30 How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?
* A. Guides the model's response using predefined prompts
* B. Customizes the model architecture
* C. Trains a model from scratch
* D. Involves post-processing model outputs and optimizing hyper parameters
Answer: AExplanation:
Prompt engineering is the art of designing natural language instructions or queries that can elicit the desired response from a large language model. Prompt engineering does not modify the model parameters or architecture, but rather relies on the model's existing knowledge and capabilities. Prompt engineering can be used to perform various tasks such as text generation, sentiment analysis, and code completion, by providing the model with the appropriate context, format, and constraints67. Prompt engineering is also known as zero-shot learning or query-based learning. Reference: [2211.01910] Large Language Models Are Human-Level Prompt Engineers](https://arxiv.org/abs/2211.01910), A developer's guide to prompt engineering and LLMs - The GitHub Blog
NEW QUESTION # 31 What is the purpose of Attention Mechanism in Transformer architecture?
* A. Apply a specific function to each word individually.
* B. Weigh the importance of different words within a sequence and understand the context.
* C. Break down a sentence into smaller pieces called tokens.
* D. Convert tokens into numerical forms (vectors) that the model can understand.
Answer: BExplanation:
The attention mechanism in the Transformer architecture is a technique that allows the model to focus on the most relevant parts of the input and output sequences. It computes a weighted sum of the input or output embeddings, where the weights indicate how much each word contributes to the representation of the current word. The attention mechanism helps the model capture the long-range dependencies and the semantic relationships between words in a sequence12. Reference: The Transformer Attention Mechanism - MachineLearningMastery.com, Attention Mechanism in the Transformers Model - Baeldung
NEW QUESTION # 32......
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