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Most Recent Oracle 1Z0-1122-23 Exam Dumps

 

Prepare for the Oracle Cloud Infrastructure 2023 AI Foundations Associate exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.

QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Oracle 1Z0-1122-23 exam and achieve success.

The questions for 1Z0-1122-23 were last updated on May 1, 2025.
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Question No. 1

What is the purpose of fine-tuning Large Language Models?

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Correct Answer: C

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


Question No. 2

What role do tokens play in Large Language Models (LLMs)?

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Correct Answer: C

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


Question No. 3

What is the primary goal of machine learning?

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Correct Answer: A

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.Reference:Artificial Intelligence (AI) | Oracle


Question No. 4

You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients' illnesses upon admission to a hospital. The goal is to classify patients into three categories - Low Risk, Moderate Risk, and High Risk - based on their medical history and vital signs.

Which type of supervised learning algorithm is required in this scenario?

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Correct Answer: D

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 decision trees, 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.Reference::Multiclass classification - Wikipedia,Multiclass Classification- Explained in Machine Learning


Question No. 5

Which is an application of Generative Adversarial Networks (GANs) in the context of Generative AI?

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Correct Answer: A

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 ...


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