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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
Deep Learning Foundations	15%	- Deep Learning and neural networks 1. Recurrent Neural Networks, LSTMs, and sequence models 2. Convolutional Neural Networks (CNN) architectures
Introduction to OCI AI Services	20%	- OCI AI Service APIs 1. OCI Language, Vision, Speech, and Document Understanding services 2. OCI Select AI and AI service use cases
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
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
Machine Learning Foundations	15%	- Machine Learning fundamentals 1. Unsupervised learning: clustering and dimensionality reduction 2. Supervised learning: regression and classification 3. Reinforcement learning basics and model evaluation concepts
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
OCI Generative AI and Oracle 23ai	10%	- OCI Generative AI Service features 1. Oracle 23ai Vector Database integration 2. Generative AI capabilities on OCI

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q25-Q30):

NEW QUESTION # 25
Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?

A. Text Generation
B. Text Classification
C. Sentiment Analysis
D. Language Detection

Answer: A

Explanation:
The OCI Language service offers several pretrained language processing capabilities, including Text Classification, Sentiment Analysis, and Language Detection. However, it does not natively support Text Generation as a part of its core language processing capabilities. Text Generation typically involves creating new content based on input prompts, which is a feature more commonly associated with models specifically designed for natural language generation.

NEW QUESTION # 26
What is " in-context learning " in the realm of Large Language Models (LLMs)?

A. Training a model on a diverse range of tasks
B. Modifying the behavior of a pretrained LLM permanently
C. Teaching a model through zero-shot learning
D. Providing a few examples of a target task via the input prompt

Answer: D

Explanation:
" In-context learning " in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt.
This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.

NEW QUESTION # 27
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

A. Supervised learning
B. Reinforcement learning
C. Unsupervised learning
D. Active learning

Answer: C

Explanation:
Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .

NEW QUESTION # 28
What is the benefit of using embedding models in OCI Generative AI service?

A. They simplify managing databases.
B. They enable creating detailed graphics.
C. They facilitate semantic searches.
D. They optimize the use of computational resources.

Answer: C

Explanation:
Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for " financial reports, " an embedding model can understand that " quarterly earnings " is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .

NEW QUESTION # 29
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?

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

Answer: D

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 # 30
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