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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
Machine Learning Foundations	15%	- Machine Learning fundamentals 1. Supervised learning: regression and classification 2. Reinforcement learning basics and model evaluation concepts 3. Unsupervised learning: clustering and dimensionality reduction
AI Foundations	10%	- Artificial Intelligence basics and terminology 1. AI, Machine Learning, and Deep Learning relationship 2. AI applications, use cases, and responsible AI principles
OCI Generative AI and Oracle 23ai	10%	- OCI Generative AI Service features 1. Oracle 23ai Vector Database integration 2. Generative AI capabilities on OCI
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
Deep Learning Foundations	15%	- Deep Learning and neural networks 1. Convolutional Neural Networks (CNN) architectures 2. Recurrent Neural Networks, LSTMs, and sequence models
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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q20-Q25):

NEW QUESTION # 20
Emma is developing a customer support chatbot for an e-commerce website. The chatbot needs to provide accurate and up-to-date return policies, which change frequently. She initially tries fine-tuning but finds that the model still uses outdated information. Which approach should Emma use instead?

A. Prompt Engineering
B. Zero-shot Prompting
C. Reinforcement Learning
D. Retrieval-Augmented Generation (RAG)

Answer: D

Explanation:
Retrieval-Augmented Generation is the appropriate approach when a chatbot must answer using information that changes frequently. Oracle defines RAG as a technique that retrieves information from specific external data sources and augments an LLM ' s response with that retrieved context, producing grounded answers.
Oracle Docs Oracle further explains that RAG can incorporate information that is more current than the model
' s original training data and that knowledge repositories can be continually updated without retraining the underlying LLM. Oracle Docs Fine-tuning is better suited to adapting model behavior or specialization, not continuously changing factual information. Prompt engineering and zero-shot prompting control how instructions are presented but do not independently supply current return-policy data. Therefore, RAG is the correct solution for providing accurate, current, organization-specific policy responses.

NEW QUESTION # 21
How does AI enhance human efforts?

A. By processing data at a speed and effectiveness far beyond human capability
B. By increasing the physical strength of humans
C. By deleting data humans need to handle
D. By completely replacing human workers in all tasks

Answer: A

Explanation:
AI enhances human efforts by processing large volumes of data quickly and accurately, performing complex computations that would be time-consuming or impossible for humans to handle manually. This allows humans to focus on more strategic, creative, and decision-making tasks, leveraging AI ' s ability to provide insights, automate repetitive processes, and support decision-making. AI does not physically enhance human capabilities, nor does it replace human workers in all tasks. Instead, it serves as an augmentation tool, amplifying human productivity and capabilities.

NEW QUESTION # 22
Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?

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

Answer: B

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 # 23
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 allow access to unlimited database resources.
C. They are free of charge for all users.
D. They provide faster internet connection speeds.

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 # 24
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

A. It is ideal for tasks such as text-to-speech conversion.
B. It delivers exceptional performance and scalability for complex AI tasks.
C. It offers seamless integration with social media platforms.
D. It provides a cost-effective solution for simple AI tasks.

Answer: B

Explanation:
Oracle Cloud Infrastructure Supercluster is designed to deliver exceptional performance and scalability for complex AI tasks. The primary benefit of this infrastructure is its ability to handle demanding AI workloads, offering high-performance computing (HPC) capabilities that are crucial for training large-scale AI models and processing massive datasets. The architecture of the Supercluster ensures low-latency networking, efficient resource allocation, and high-throughput processing, making it ideal for AI tasks that require significant computational power, such as deep learning, data analytics, and large-scale simulations.

NEW QUESTION # 25
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