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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Objectives
Artificial Intelligence and Machine Learning Fundamentals	- Deep Learning Fundamentals 1. Convolutional and sequence models 2. Neural networks - Artificial Intelligence Concepts 1. AI use cases and applications 2. AI fundamentals and terminology - Machine Learning Fundamentals 1. Supervised and unsupervised learning 2. Machine learning models and architectures
Oracle AI and Machine Learning Services	- Oracle AI Stack 1. AI data and machine learning services 2. AI infrastructure - OCI AI Services 1. Language 2. Speech 3. Document Understanding 4. Vision - OCI Machine Learning Services 1. OCI Data Science and machine learning workflows 2. Machine learning capabilities in OCI
OCI Generative AI and Oracle Database AI Capabilities	- Oracle AI Database 1. Vector database concepts 2. AI capabilities in Oracle Database - AI Application Frameworks 1. Retrieval and AI application concepts 2. Language frameworks - OCI Generative AI 1. OCI Generative AI services 2. Generative AI models and applications
Generative AI and Large Language Models	- Large Language Models 1. Language models and generative AI applications 2. LLM fundamentals - Generative AI Fundamentals 1. Generative AI concepts and capabilities 2. Generative AI use cases

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q21-Q26):

NEW QUESTION # 21
What distinguishes Generative AI from other types of AI?

A. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
B. Generative AI uses algorithms to predict outcomes based on past data.
C. Generative AI focuses on making decisions based on user interactions.
D. Generative AI involves training models to perform tasks without human intervention.

Answer: A

Explanation:
Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.

NEW QUESTION # 22
What does " fine-tuning " refer to in the context of OCI Generative AI service?

A. Adjusting the model parameters to improve accuracy
B. Doubling the neural network layers
C. Upgrading the hardware of the AI clusters
D. Encrypting the data for security reasons

Answer: A

Explanation:
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.

NEW QUESTION # 23
What key objective does machine learning strive to achieve?

A. Explicitly programming computers
B. Improving computer hardware
C. Enabling computers to learn and improve from experience
D. Creating algorithms to solve complex problems

Answer: C

Explanation:
The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task.
As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.

NEW QUESTION # 24
What is the benefit of using embedding models in OCI Generative AI service?

A. They optimize the use of computational resources.
B. They facilitate semantic searches.
C. They enable creating detailed graphics.
D. They simplify managing databases.

Answer: B

Explanation:
Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for " financial reports, " an embedding model can understand that " quarterly earnings " is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .

NEW QUESTION # 25
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?

A. Binary Classification
B. Clustering
C. Regression
D. Multi-Class Classification

Answer: D

Explanation:
In this healthcare scenario, where the goal is to classify patients into three categories-Low Risk, Moderate Risk, and High Risk-based on their medical history and vital signs, a Multi-Class Classification algorithm is required. Multi-class classification is a type of supervised learning algorithm used when there are three or more classes or categories to predict. This method is well-suited for situations where each instance needs to be classified into one of several categories, which aligns with the requirement to categorize patients into different risk levels.

NEW QUESTION # 26
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