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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
OCI Generative AI and Oracle 23ai	10%	- OCI Generative AI Service features 1. Oracle 23ai Vector Database integration 2. Generative AI capabilities on OCI
AI Foundations	10%	- Artificial Intelligence basics and terminology 1. AI applications, use cases, and responsible AI principles 2. AI, Machine Learning, and Deep Learning relationship
Deep Learning Foundations	15%	- Deep Learning and neural networks 1. Recurrent Neural Networks, LSTMs, and sequence models 2. Convolutional Neural Networks (CNN) architectures
Generative AI and Large Language Models	15%	- Generative AI concepts 1. Transformers, prompt engineering, and fine-tuning 2. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
Machine Learning Foundations	15%	- Machine Learning fundamentals 1. Supervised learning: regression and classification 2. Unsupervised learning: clustering and dimensionality reduction 3. Reinforcement learning basics and model evaluation concepts
Introduction to OCI AI Services	20%	- OCI AI Service APIs 1. OCI Select AI and AI service use cases 2. OCI Language, Vision, Speech, and Document Understanding services
OCI AI Portfolio	15%	- Overview of OCI AI offerings 1. AI Services, ML Services, and AI Infrastructure overview 2. OCI Data Science and GPU-based compute infrastructure

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q15-Q20):

NEW QUESTION # 15
Which AI domain can be employed for identifying patterns in images and extract relevant features?

A. Computer Vision
B. Speech Processing
C. Anomaly Detection
D. Natural Language Processing

Answer: A

Explanation:
Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.

NEW QUESTION # 16
What distinguishes Generative AI from other types of AI?

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

Answer: D

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 # 17
What is the purpose of the model catalog in OCI Data Science?

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

Answer: D

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 # 18
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

A. Chat models
B. Translation models
C. Embedding models
D. Generation models

Answer: B

Explanation:
The OCI Generative AI service offers various categories of pretrained foundational models, including Embedding models, Chat models, and Generation models. These models are designed to perform a wide range of tasks, such as generating text, answering questions, and providing contextual embeddings. However, Translation models, which are typically used for converting text from one language to another, are not a category available in the OCI Generative AI service ' s current offerings. The focus of the OCI Generative AI service is more aligned with tasks related to text generation, chat interactions, and embedding generation rather than direct language translation.

NEW QUESTION # 19
In machine learning, what does the term " model training " mean?

A. Writing code for the entire program
B. Establishing a relationship between input features and output
C. Performing data analysis on collected and labeled data
D. Analyzing the accuracy of a trained model

Answer: B

Explanation:
In machine learning, " model training " refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.

NEW QUESTION # 20
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