Most Recent Oracle 1Z0-1122-23 Exam Questions & Answers

Generative Adversarial Networks (GANs) are a type of AI model that can generate realistic images that resemble training data. The architecture of a GAN consists of two separate neural networks that are pitted against each other in a game-like scenario. The first network, known as the generator network, tries to create fake data that looks real. The second network, known as the discriminator network, tries to distinguish between real and fake data. The generator network learns from the feedback of the discriminator network and tries to fool it by improving the quality of the fake data. The discriminator network also learns from the feedback of the generator network and tries to improve its accuracy.The process continues until the generator network produces data that is indistinguishable from the real data4.GANs can be used to create realistic images of faces, animals, landscapes, and more5.Reference:Generative models - OpenAI,Artificial Intelligence Explained: What Are Generative Adversarial ...

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 textReference::Language Overview - Oracle,AI Text Analysis at Scale | Oracle

Computer Vision is an AI domain that is associated with tasks such as recognizing faces in images and classifying objects. Computer vision is a field of artificial intelligence that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs, and to take actions or make recommendations based on that information. Computer vision works by applying machine learning and deep learning models to visual data, such as pixels, colors, shapes, textures, etc., and extracting features and patterns that can be used for various purposes. Some of the common techniques and applications of computer vision are:

Face recognition: Identifying or verifying the identity of a person based on their facial features.

Object detection: Locating and labeling objects of interest in an image or a video.

Object recognition: Classifying objects into predefined categories, such as animals, vehicles, fruits, etc.

Scene understanding: Analyzing the context and semantics of a visual scene, such as the location, time, weather, activity, etc.

Image segmentation: Partitioning an image into multiple regions that share similar characteristics, such as color, texture, shape, etc.

Image enhancement: Improving the quality or appearance of an image by applying filters, transformations, or corrections.

Image generation: Creating realistic or stylized images from scratch or based on some input data, such as sketches, captions, or attributes.Reference::What is Computer Vision? | IBM,Computer vision - Wikipedia

Fine-tuning is the process of updating the model parameters on a new task and dataset, using a pre-trained large language model as the starting point. Fine-tuning allows the model to adapt to the specific context and domain of the new task, and improve its performance and accuracy.Fine-tuning can be used to customize the model's capabilities for specific tasks such as text classification, named entity recognition, and machine translation82. Fine-tuning is also known as transfer learning or task-based learning.Reference:A Complete Guide to Fine Tuning Large Language Models,Finetuning Large Language Models - DeepLearning.AI

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