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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q17-Q22):

NEW QUESTION # 17
Lisa is working on a project that involves transcribing thousands of audio files stored in Oracle Cloud. She wants to process multiple files efficiently instead of transcribing them one by one. Which OCI Speech feature should Lisa use?

A. Timestamping
B. Profanity filtering
C. Confidence scoring
D. Batch support

Answer: D

Explanation:
OCI Speech supports batch transcription for efficiently processing prerecorded media. Lisa ' s key requirement is to process thousands of audio files without manually handling them individually. Oracle documentation explicitly identifies OCI Speech as supporting both real-time and batch transcription, while the OCI Speech documentation includes transcription jobs and large batch jobs among its supported workflows.
Oracle Docs Batch support therefore directly addresses high-volume processing of stored audio. Confidence scoring measures the service ' s certainty about recognized speech, timestamping associates transcript content with positions in the source recording, and profanity filtering controls how offensive language is represented.
Those capabilities enhance transcription output but do not provide the required multi-file processing mechanism. Consequently, Batch support is the appropriate OCI Speech capability for scalable transcription of Lisa ' s collection of audio files.

NEW QUESTION # 18
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?

A. By analyzing sentiment in text documents
B. By automating data extraction from documents
C. By transcribing spoken language
D. By generating lifelike speech from documents

Answer: B

Explanation:
Oracle Cloud Infrastructure (OCI) Document Understanding service facilitates business processes by automating data extraction from documents. This service leverages machine learning to identify, classify, and extract relevant information from various document types, reducing the need for manual data entry and improving efficiency in document processing workflows. Automation of these tasks enables organizations to streamline operations and reduce errors associated with manual data handling.

NEW QUESTION # 19
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

A. Reinforcement learning
B. Supervised 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 # 20
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

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

Answer: D

NEW QUESTION # 21
How does AI enhance human efforts?

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

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 # 22
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