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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: OCI Generative AI and Oracle 23ai	10%	- Discuss Autonomous Database Select AI - Describe OCI Generative AI Services - Discuss Oracle Vector Search
Topic 2: Intro to OCI AI Services	20%	- OCI Language - OCI Speech - OCI Document Understanding - OCI Vision
Topic 3: Intro to DL Foundations	15%	- Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN) - Discuss Deep Learning Fundamentals
Topic 4: Intro to AI Foundations	10%	- Explain AI vs ML vs DL - Discuss AI Applications and Types of Data - Discuss AI Basics
Topic 5: Intro to Generative AI and LLMs	15%	- Explain Prompt Engineering and Instruction Tuning - Discuss Large Language Models Fundamentals - Explain LLM Fine Tuning - Discuss Generative AI Overview - Explain Transformers Fundamentals
Topic 6: Get started with OCI AI Portfolio	15%	- Discuss OCI AI Services Overview - Discuss OCI AI Infrastructure Overview - Explain Responsible AI - Discuss OCI ML Services Overview
Topic 7: Intro to ML Foundations	15%	- Discuss Unsupervised Learning Fundamentals - Discuss Reinforcement Learning Fundamentals - Discuss Supervised Learning Fundamentals 1. Regression 2. Classification - Explain Machine Learning Basics

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

NEW QUESTION # 20
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

A. AI, ML, and DL are entirely separate fields with no overlap.
B. AI is a subset of DL, which is a subset of ML.
C. DL is a subset of AI, and ML is a subset of DL.
D. ML is a subset of AI, and DL is a subset of ML.

Answer: D

Explanation:
Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence " deep " ).
In this hierarchy:
* AI includes all methods to make machines intelligent.
* ML refers to the methods within AI that focus on learning from data.
* DL is a specialized field within ML that deals with deep neural networks.

NEW QUESTION # 21
What is the purpose of the model catalog in OCI Data Science?

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

Answer: C

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 # 22
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?

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

Answer: D

Explanation:
Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.

NEW QUESTION # 23
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

A. They disregard model size and prioritize high-quality data only.
B. They ensure that the model size, training time, and data size are balanced for optimal results.
C. They prioritize larger model sizes to achieve better performance.
D. They focus on increasing the number of tokens while keeping the model size constant.

Answer: B

Explanation:
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.

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 offers seamless integration with social media platforms.
C. It provides a cost-effective solution for simple AI tasks.
D. It delivers exceptional performance and scalability for complex AI tasks.

Answer: D

NEW QUESTION # 25
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