1z0-1122-23 Oracle Cloud Infrastructure 2023 AI Foundations Associate Question and Answers

Model training is the process of finding the optimal values for the model parameters that minimize the error between the model predictions and the actual output. This is done by using a learning algorithm that iteratively updates the parameters based on the input features and the output1. References: Oracle Cloud Infrastructure Documentation

Oracle Cloud Infrastructure Document Understanding service is a cloud-based AI service for automating data extraction from documents. It can process various types of documents, such as invoices, receipts, contracts, forms, etc., and extract key information fields from them using optical character recognition (OCR) and natural language understanding (NLU) techniques. It can also provide confidence scores for each extracted field and enable human verification if needed. By using this service, businesses can reduce manual efforts, improve accuracy, and accelerate workflows that involve document processing. Some of the use cases for Oracle Cloud Infrastructure Document Understanding service are:

• Invoice Processing: Extract invoice details, such as invoice number, date, amount, vendor name, etc., and validate them against purchase orders or contracts.
• Contract Analysis: Extract contract terms, such as parties, duration, clauses, obligations, etc., and compare them with standard templates or policies.
• Form Processing: Extract form fields, such as name, address, phone number, email, etc., and populate them into databases or applications. References: : [Document Understanding Overview - Oracle], [AI Document Understanding at Scale | Oracle]

Oracle Cloud Infrastructure Anomaly Detection is an AI service that provides real-time and batch anomaly detection for univariate and multivariate time series data. Through a simple user interface, organizations can create and train models to detect anomalies and identify unusual behavior, changes in trends, outliers, and more. Anomaly Detection can contribute to fraud detection by analyzing data from various sources, such as transactions, logs, sensors, or customer behavior, and alerting users when suspicious or fraudulent activities are detected2. References: Anomaly Detection | Oracle

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 artificialintelligence 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. References: : What is Natural Language Processing? | IBM, Natural language processing - Wikipedia

 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. References: [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

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. References: Oracle Cloud Infrastructure 2023 AI Foundations Associate | Oracle University