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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q30-Q35):

NEW QUESTION # 30
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

A. They focus on increasing the number of tokens while keeping the model size constant.
B. They disregard model size and prioritize high-quality data only.
C. They ensure that the model size, training time, and data size are balanced for optimal results.
D. They prioritize larger model sizes to achieve better performance.

Answer: C

Explanation:
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.

NEW QUESTION # 31
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. Regression
B. Multi-Class Classification
C. Clustering
D. Binary Classification

Answer: B

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 # 32
What is the primary purpose of reinforcement learning?

A. Making predictions from labeled data
B. Learning from outcomes to make decisions
C. Identifying patterns in data
D. Finding relationships within data sets

Answer: B

Explanation:
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.

NEW QUESTION # 33
What would you use Oracle AI Vector Search for?

A. Query data based on keywords.
B. Manage database security protocols.
C. Query data based on semantics.
D. Store business data in a cloud database.

Answer: C

Explanation:
Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .

NEW QUESTION # 34
What are Convolutional Neural Networks (CNNs) primarily used for?

A. Text processing
B. Image classification
C. Image generation
D. Time series prediction

Answer: B

Explanation:
Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.
CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.

NEW QUESTION # 35
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