1z0-1122-23 Sample Questions Answers

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

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]

Classification and regression are two subtypes of supervised learning in machine learning. The main difference between them is the type of output variable they deal with. Classification assigns data points to discrete categories based on some criteria or rules. For example, classifying emails into spam or not spam based on their content is a classification problem because the output variable is binary (spam or not spam). Regression predicts continuous values for data points based on their input features. For example, predicting house prices based on their size, location, amenities, etc., is a regression problem because the output variable is continuous (house price). Classification and regression use different types of algorithms and metrics to evaluate their performance. References: : Oracle Cloud Infrastructure AI - Machine Learning Concepts, Classification vs Regression in Machine Learning | by …

Oracle Cloud Infrastructure Speech is an AI service that applies automatic speech recognition (ASR) technology to transform audio-based content into text. Developers can easily make API calls to integrate Speech’s pretrained models into their applications. Speech can be used for accurate, text-normalized, time-stamped transcription via the console and REST APIs as well as command-line interfaces or SDKs. You can also use Speech in an OCI Data Science notebook session. With Speech, you can filter profanities, get confidence scores for both single words and complete transcriptions, and more1. References: Speech AI Service that Uses ASR | OCI Speech - Oracle

Reinforcement learning is a type of machine learning that is based on learning from outcomes to make decisions. Reinforcement learning algorithms learn from their own actions and experiences in an environment, rather than from labeled data or explicit feedback. The goal of reinforcement learning is to find an optimal policy that maximizes a cumulative reward over time. A policy is a rule that determines what action to take ineach state of the environment. A reward is a feedback signal that indicates how good or bad an action was for achieving a desired objective. Reinforcement learning involves a trial-and-error process of exploring different actions and observing their consequences, and then updating the policy accordingly. Some of the challenges and components of reinforcement learning are:

• Exploration vs exploitation: Balancing between trying new actions that might lead to higher rewards in the future (exploration) and choosing known actions that yield immediate rewards (exploitation).
• Markov decision process (MDP): A mathematical framework for modeling sequential decision making problems under uncertainty, where the outcomes depend only on the current state and action, not on the previous ones.
• Value function: A function that estimates the expected long-term return of each state or state-action pair, based on the current policy.
• Q-learning: A popular reinforcement learning algorithm that learns a value function called Q-function, which represents the quality of taking a certain action in a certain state.
• Deep reinforcement learning: A branch of reinforcement learning that combines deep neural networks with reinforcement learning algorithms to handle complex and high-dimensional problems, such as playing video games or controlling robots. References: : Reinforcement learning - Wikipedia, What is Reinforcement Learning? – Overview of How it Works - Synopsys

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