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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Objectives
Artificial Intelligence and Machine Learning Fundamentals	- Artificial Intelligence Concepts 1. AI use cases and applications 2. AI fundamentals and terminology - Deep Learning Fundamentals 1. Neural networks 2. Convolutional and sequence models - Machine Learning Fundamentals 1. Supervised and unsupervised learning 2. Machine learning models and architectures
Generative AI and Large Language Models	- Large Language Models 1. LLM fundamentals 2. Language models and generative AI applications - Generative AI Fundamentals 1. Generative AI use cases 2. Generative AI concepts and capabilities
Oracle AI and Machine Learning Services	- OCI AI Services 1. Speech 2. Language 3. Vision 4. Document Understanding - Oracle AI Stack 1. AI infrastructure 2. AI data and machine learning services - OCI Machine Learning Services 1. OCI Data Science and machine learning workflows 2. Machine learning capabilities in OCI
OCI Generative AI and Oracle Database AI Capabilities	- OCI Generative AI 1. OCI Generative AI services 2. Generative AI models and applications - AI Application Frameworks 1. Retrieval and AI application concepts 2. Language frameworks - Oracle AI Database 1. Vector database concepts 2. AI capabilities in Oracle Database

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q11-Q16):

NEW QUESTION # 11
What is the purpose of the model catalog in OCI Data Science?

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

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 # 12
Which feature of OCI Speech helps make transcriptions easier to read and understand?

A. Timestamping
B. Audio tuning
C. Profanity filtering
D. Text normalization

Answer: D

Explanation:
The text normalization feature of OCI Speech helps make transcriptions easier to read and understand by converting spoken language into a more standardized and grammatically correct format. This process includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear, accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions, making them more accessible and easier to process in downstream applications.
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NEW QUESTION # 13
How does AI enhance human efforts?

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

Answer: B

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 # 14
Which feature is NOT available as part of OCI Speech capabilities?

A. Supports multiple languages including English, Spanish, and Portuguese
B. Transcribes audio and video files into text
C. Provides timestamped, grammatically accurate transcriptions
D. Uses extensive data science experience to operate

Answer: D

Explanation:
OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs.
These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.

NEW QUESTION # 15
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. Unsupervised learning
C. Reinforcement learning
D. Active learning

Answer: B

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 # 16
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