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Oracle Cloud Infrastructure 2023 AI Foundations Associate Sample Questions (Q27-Q32):

NEW QUESTION # 27
What is the purpose of Attention Mechanism in Transformer architecture?

* A. Weigh the importance of different words within a sequence and understand the context.
* B. Convert tokens into numerical forms (vectors) that the model can understand.
* C. Break down a sentence into smaller pieces called tokens.
* D. Apply a specific function to each word individually.
Answer: A

Explanation:
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 # 28
What is the difference between Large Language Models (LLMs) and traditional machine learning models?

* A. LLMs are specifically designed for natural language processing and understanding.
* B. LLMs require labeled output for training.
* C. LLMs have a limited number of parameters compared to other models.
* D. LLMs focus on image recognition tasks.
Answer: A

Explanation:
Large language models (LLMs) are a class of deep learning models that can recognize and generate natural language, among other tasks. LLMs are trained on huge sets of text data, learning grammar, semantics, and context. LLMs use the Transformer architecture, which relies on self-attention to process and understand the input and output sequences. LLMs can perform various natural language processing and understanding tasks based on the input provided, such as text summarization, question answering, text generation, and more34. Traditional machine learning models, on the other hand, are usually trained with specific statistical algorithms that deliver pre-defined outcomes. They often require labeled data and feature engineering, and they are not as flexible and adaptable as LLMs5. Reference: What are LLMs, and how are they used in generative AI?, An Introduction to LLMOps: Operationalizing and Managing Large Language Models using Azure ML, An Introduction to Large Language Models (LLMs): How It Got ... - Labellerr

NEW QUESTION # 29
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?

* A. Speech Processing
* B. Anomaly Detection
* C. Natural Language Processing
* D. Computer Vision
Answer: C

Explanation:
Natural Language Processing (NLP) is an AI domain that is associated with tasks such as identifying the sentiment of text and translating text between languages. NLP is an interdisciplinary field that combines computer science, linguistics, and artificial intelligence to enable computers to process and understand natural language data, such as text or speech. NLP involves various techniques and applications, such as:
Text analysis: Extracting meaningful information from text data, such as keywords, entities, topics, sentiments, emotions, etc.
Text generation: Producing natural language text from structured or unstructured data, such as summaries, captions, headlines, stories, etc.
Machine translation: Translating text or speech from one language to another automatically and accurately.
Question answering: Retrieving relevant answers to natural language questions from a knowledge base or a document collection.
Speech recognition: Converting speech signals into text or commands.
Speech synthesis: Converting text into speech signals with natural sounding voices.
Natural language understanding: Interpreting the meaning and intent of natural language inputs and generating appropriate responses.
Natural language generation: Creating natural language outputs that are coherent, fluent, and relevant to the context. Reference: : What is Natural Language Processing? | IBM, Natural language processing - Wikipedia

NEW QUESTION # 30
Which capability is supported by Oracle Cloud Infrastructure Language service?

* A. Converting text into images
* B. Analyzing text to extract structured information like sentiment or entities
* C. Translating speech into text
* D. Detecting objects and scenes in Images
Answer: B

Explanation:
Oracle Cloud Infrastructure Language service is a cloud-based AI service for performing sophisticated text analysis at scale. It provides various capabilities to process unstructured text and extract structured information like sentiment or entities using natural language processing techniques. Some of the capabilities supported by Oracle Cloud Infrastructure Language service are:
Language Detection: Detects languages based on the provided text, and includes a confidence score.
Text Classification: Identifies the document category and subcategory that the text belongs to.
Named Entity Recognition: Identifies common entities, people, places, locations, email, and so on.
Key Phrase Extraction: Extracts an important set of phrases from a block of text.
Sentiment Analysis: Identifies aspects from the provided text and classifies each into positive, negative, or neutral polarity.
Text Translation: Translates text into the language of your choice.
Personal Identifiable Information: Identifies, classifies, and de-identifies private information in unstructured text Reference: : Language Overview - Oracle, AI Text Analysis at Scale | Oracle

NEW QUESTION # 31
What role do tokens play in Large Language Models (LLMs)?

* A. They are used to define the architecture of the model's neural network.
* B. They are Individual units into which a piece of text is divided during processing by the model.
* C. They represent the numerical values of model parameters.
* D. They determine the size of the model's memory.
Answer: B

Explanation:
Tokens are the basic units of text representation in large language models. They can be words, subwords, characters, or symbols. Tokens are used to encode the input text into numerical vectors that can be processed by the model's neural network. Tokens also determine the vocabulary size and the maximum sequence length of the model3. Reference: Oracle Cloud Infrastructure 2023 AI Foundations Associate | Oracle University

NEW QUESTION # 32
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