Exam Code: 1z0-1122-26
Exam Name: Oracle Cloud Infrastructure 2026 AI Foundations Associate
Certification Provider: Oracle
Corresponding Certification: Oracle Cloud Infrastructure

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Name: Oracle 1z0-1122-26 Exam
Brand: Oracle
SKU: 1z0-1122-26
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Updated: Sep 08, 2026

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Oracle 1z0-1122-26 Exam Overview:

Certification Vendor:	Oracle
Exam Name:	Oracle Cloud Infrastructure 2026 AI Foundations Associate
Exam Number:	1Z0-1122-26
Passing Score:	65%
Exam Price:	Free (with completed learning path); $245 USD without learning path
Related Certifications:	Oracle AI Database Foundations Associate (1Z0-1195-26) Oracle Cloud Infrastructure 2026 Foundations Associate (1Z0-1085-26)
Available Languages:	English
Certificate Validity Period:	24 months from date earned
Exam Duration:	60 minutes
Real Exam Qty:	40
Exam Format:	Multiple Choice
Recommended Training:	Become an OCI 2026 AI Foundations Associate (Official Learning Path) Oracle Cloud Infrastructure AI Foundations (Free Course)
Exam Registration:	Oracle MyLearn Portal Oracle University Certification Registration
Sample Questions:	Oracle 1z0-1122-26 Sample Questions
Exam Way:	Online proctored / Onsite at authorized testing centers
Pre Condition:	No prerequisites; no prior AI, ML, or cloud experience required
Official Syllabus URL:	https://education.oracle.com/oracle-cloud-infrastructure-2026-ai-foundations-associate/pexam_1Z0-1122-26

Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
OCI AI Portfolio	15%	- Overview of OCI AI offerings 1. OCI Data Science and GPU-based compute infrastructure 2. AI Services, ML Services, and AI Infrastructure overview
Generative AI and Large Language Models	15%	- Generative AI concepts 1. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs 2. Transformers, prompt engineering, and fine-tuning
OCI Generative AI and Oracle 23ai	10%	- OCI Generative AI Service features 1. Generative AI capabilities on OCI 2. Oracle 23ai Vector Database integration
Machine Learning Foundations	15%	- Machine Learning fundamentals 1. Unsupervised learning: clustering and dimensionality reduction 2. Reinforcement learning basics and model evaluation concepts 3. Supervised learning: regression and classification
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
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

Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions:

Question 1

How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?

A. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
B. Prompt Engineering modifies training data, while Fine-tuning alters the model ' s structure.
C. Both involve retraining the model, but Prompt Engineering does it more often.
D. Prompt Engineering adjusts the model ' s parameters, while Fine-tuning crafts input prompts.

Question 2

In machine learning, what does the term " model training " mean?

A. Analyzing the accuracy of a trained model
B. Writing code for the entire program
C. Performing data analysis on collected and labeled data
D. Establishing a relationship between input features and output

Question 3

Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

A. Embedding models
B. Generation models
C. Translation models
D. Chat models

Question 4

What is the purpose of the model catalog in OCI Data Science?

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

Question 5

You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients ' illnesses upon admission to a hospital. The goal is to classify patients into three categories - Low Risk, Moderate Risk, and High Risk - based on their medical history and vital signs. Which type of supervised learning algorithm is required in this scenario?

A. Multi-Class Classification
B. Regression
C. Binary Classification
D. Clustering

Solutions:

Question 1
Answer: A

Question 2
Answer: D

Question 3
Answer: C

Question 4
Answer: B

Question 5
Answer: A

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