Oracle 1z0-1122-23 Oracle Cloud Infrastructure 2023 AI Foundations Associate Exam Practice Test

Multi-class classification is a type of supervised learning algorithm that is required in this scenario because the output variable has more than two classes. Multi-class classification is the problem of classifying instances into one of three or more classes. For example, classifying patients into low risk, moderate risk, or high risk based on their medical history and vital signs is a multi-class classification problem because each patient can only belong to one of these three classes. Multi-class classification can be solved by using various algorithms, such as decisiontrees, random forests, support vector machines (SVMs), k-nearest neighbors (k-NN), naive Bayes, logistic regression, neural networks, etc. Some of these algorithms can naturally handle multi-class problems, while others need to be adapted by using strategies such as one-vs-one or one-vs-rest. References: : Multiclass classification - Wikipedia, Multiclass Classification- Explained in Machine Learning

 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

Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that is particularly well-suited for image recognition and processing tasks. They are made up of multiple layers, including convolutional layers, pooling layers, and fully connected layers. The convolutional layer is the core building block of a CNN, and it is where the majority of computation occurs. It requires a few components, which are input data, a filter, and a feature map. The filter is a small matrix of weights that slides over the input data and performs element-wise multiplication and summation, resulting in a feature map that represents the activation of a certain feature in the input. By applying multiple filters, the CNN can detect different patterns in the image, such as edges, shapes, colors, textures, etc. The pooling layer is used to reduce the spatial dimensionality of the feature maps, while preserving the most important information. The fully connected layer is the final layer of a CNN, and it is where the classification or regression task is performed based on the extracted features. CNNs can learn to detect complex patterns in images by adjusting their weights during training using backpropagation and gradient descent algorithms. References: : Convolutional neural network - Wikipedia, What are Convolutional Neural Networks? | IBM, Convolutional Neural Network (CNN) in Machine Learning

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. References: 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

Oracle Cloud Infrastructure Supercluster is a cloud service that provides ultrafast cluster networking, HPC storage, and OCI Compute bare metal instances. OCI Supercluster is ideal for training generative AI, including conversational applications and diffusion models, as it can deploy up to tens of thousands of NVIDIA GPUs per cluster for much greater scalability than similar offerings from other providers. OCI Supercluster also reduces the time needed to train AI models with simple Ethernet network architecture that provides ultrahigh performance at massive scale. Additionally, OCI Supercluster offers cost savings and access to AI subjectmatter experts56. References: OCI Supercluster and AI Infrastructure | Oracle, Oracle Delivers More Choices for AI Infrastructure and General-Purpose …

Unsupervised learning is a type of machine learning that is used to understand relationships within data and is not focused on making predictions or classifications. Unsupervised learning algorithms work with unlabeled data, which means the data does not have predefined categories or outcomes. The goal of unsupervised learning is to discover hidden patterns, structures, or features in the data that can provide valuable insights or reduce complexity. Some of the common techniques and applications of unsupervised learning are:

• Clustering: Grouping similar data points together based on their attributes or distances. For example, clustering can be used to segment customers based on their preferences, behavior, or demographics.
• Dimensionality reduction: Reducing the number of variables or features in a dataset while preserving the essential information. For example, dimensionality reduction can be used to compress images, remove noise, or visualize high-dimensional data in lower dimensions.
• Anomaly detection: Identifying outliers or abnormal data points that deviate from the normal distribution or behavior of the data. For example, anomaly detection can be used to detect fraud, network intrusion, or system failure.
• Association rule mining: Finding rules that describe how variables or items are related or co-occur in a dataset. For example, association rule mining can be used to discover frequent itemsets in market basket analysis or recommend products based on purchase history. References: : Unsupervised learning - Wikipedia, What is Unsupervised Learning? | IBM

Machine learning is a branch of artificial intelligence that enables computers to learn from data and experience without being explicitly programmed. Machine learning algorithms can adapt to new data and situations and improve their performance over time2. References: Artificial Intelligence (AI) | Oracle

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