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Oracle 1Z0-1122-25 Exam Syllabus Topics:

Topic	Details
Topic 1	Intro to AI Foundations: This section of the exam measures the skills of AI Practitioners and Data Analysts in understanding the fundamentals of artificial intelligence. It covers key concepts, AI applications across industries, and the types of data used in AI models. It also explains the differences between artificial intelligence, machine learning, and deep learning, providing clarity on how these technologies interact and complement each other.
Topic 2	Intro to Generative AI & LLMs: This section tests the abilities of AI Developers to understand generative AI and large language models. It introduces the principles of generative AI, explains the fundamentals of large language models (LLMs), and discusses the core workings of transformers, prompt engineering, instruction tuning, and LLM fine-tuning for optimizing AI-generated content.
Topic 3	Intro to OCI AI Services: This section tests the expertise of AI Solutions Engineers in working with OCI AI services and related APIs. It provides insights into key AI services such as language processing, computer vision, document understanding, and speech recognition, allowing professionals to leverage Oracle’s AI ecosystem for building intelligent applications.
Topic 4	OCI Generative AI and Oracle 23ai: This section evaluates the skills of Cloud AI Architects in utilizing Oracle’s generative AI capabilities. It includes a deep dive into OCI Generative AI services, Autonomous Database Select AI for enhanced data intelligence and Oracle Vector Search for efficient information retrieval in AI-driven applications.
Topic 5	Get started with OCI AI Portfolio: This section measures the proficiency of Cloud AI Specialists in exploring Oracle Cloud Infrastructure (OCI) AI services. It provides an overview of OCI AI and machine learning services, details AI infrastructure capabilities and explains responsible AI principles to ensure ethical and transparent AI development.
Topic 6	Intro to DL Foundations: This section assesses the expertise of Deep Learning Engineers in understanding deep learning frameworks and architectures. It covers fundamental concepts of deep learning, introduces convolutional neural networks (CNN) for image processing, and explores sequence models like recurrent neural networks (RNN) and long short-term memory (LSTM) networks for handling sequential data.

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q18-Q23):

NEW QUESTION # 18
What is the difference between classification and regression in Supervised Machine Learning?

A. Classification and regression both assign data points to categories.
B. Classification and regression both predict continuous values.
C. Classification assigns data points to categories, whereas regression predicts continuous values.
D. Classification predicts continuous values, whereas regression assigns data points to categories.

Answer: C

Explanation:
In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either "spam" or "not spam." On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers "which category?" regression answers "how much?" or "what value?".

NEW QUESTION # 19
What is the purpose of the model catalog in OCI Data Science?

A. To store, track, share, and manage models
B. To deploy models as HTTP endpoints
C. To provide a preinstalled open source library
D. To create and switch between different environments

Answer: A

Explanation:
The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage machine learning models. This functionality is essential for maintaining an organized repository where data scientists and developers can collaborate on models, monitor their performance, and manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most recent and effective models are available for deployment. This capability is crucial in a collaborative environment where multiple stakeholders need access to the latest model versions for testing, evaluation, and deployment.

NEW QUESTION # 20
Which is NOT a capability of OCI Vision's image analysis?

A. Object detection with bounding boxes
B. Assigning classification labels to images
C. Locating and extracting text in images
D. Translating text in images to another language

Answer: D

Explanation:
OCI Vision's image analysis capabilities include locating and extracting text from images, assigning classification labels to images, and detecting objects with bounding boxes. However, translating text in images to another language is not a capability of OCI Vision's image analysis. This functionality typically requires an additional layer of processing, such as integration with a language translation service, which is beyond the scope of OCI Vision's core image analysis features.
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NEW QUESTION # 21
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. Multi-Class Classification
D. Regression

Answer: C

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 # 22
What is the purpose of Attention Mechanism in Transformer architecture?

A. Weigh the importance of different words within a sequence and understand the context.
B. Break down a sentence into smaller pieces called tokens.
C. Apply a specific function to each word individually.
D. Convert tokens into numerical forms (vectors) that the model can understand.

Answer: A

Explanation:
The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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NEW QUESTION # 23
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The Oracle 1Z0-1122-25 certification exam syllabus is changing with the passage of time. As a 1Z0-1122-25 exam candidate you have to be aware of these Oracle 1Z0-1122-25 exam changes. To give you complete knowledge about the Oracle 1Z0-1122-25 Exam Topics, the GetValidTest has hired a team of experts that consistently work on these changes and add these changes in Oracle 1Z0-1122-25 exam practice test questions.

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DESKTOP-BASED ORACLE 1Z0-1122-25 PRACTICE EXAM SOFTWARE

Desktop-Based Oracle 1Z0-1122-25 Practice Exam Software