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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q46-Q51):

NEW QUESTION # 46
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. Clustering
B. Binary Classification
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 # 47
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

A. Fairness
B. Explicability
C. Prevention of harm
D. Respect for human autonomy

Answer: B

NEW QUESTION # 48
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?

A. They provide high performance compute resources for fine-tuning tasks.
B. They are free of charge for all users.
C. They provide faster internet connection speeds.
D. They allow access to unlimited database resources.

Answer: A

Explanation:
The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.

NEW QUESTION # 49
You are working on a multilingual public announcement system. Which AI task will you use to implement it?

A. Text summarization
B. Text to speech
C. Audio recording
D. Speech recognition

Answer: B

Explanation:
For a multilingual public announcement system, the AI task that would be most relevant is " Text to Speech " (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .

NEW QUESTION # 50
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

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

Answer: D

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 # 51
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