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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q38-Q43):

NEW QUESTION # 38
You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?

A. Speech
B. Language
C. Document Understanding
D. Vision

Answer: A

Explanation:
For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the " Speech " service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.

NEW QUESTION # 39
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

A. They prioritize larger model sizes to achieve better performance.
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 focus on increasing the number of tokens while keeping the model size constant.

Answer: C

NEW QUESTION # 40
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

A. Providing labels for the output neurons
B. Capturing the internal representation of the raw image data
C. Storing the input pixel values
D. Directly predicting the final output

Answer: B

Explanation:
In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.

NEW QUESTION # 41
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. Multi-Class Classification
B. Clustering
C. Regression
D. Binary Classification

Answer: A

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 # 42
What is the primary benefit of using the OCI Language service for text analysis?

A. It provides image processing capabilities.
B. It allows for text analysis at scale without machine learning expertise.
C. It requires extensive machine learning expertise to use.
D. It only works with structured data.

Answer: B

Explanation:
The primary benefit of using the OCI Language service for text analysis is its ability to scale text analysis without requiring users to have extensive machine learning expertise. The service abstracts the complexities of machine learning, allowing businesses to easily process and analyze large amounts of text data through pre- built models. This accessibility makes it possible for a broader range of users to leverage advanced text analysis capabilities, facilitating insights from textual data without needing to develop and train models from scratch.

NEW QUESTION # 43
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