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Oracle 1z0-1122-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: OCI Generative AI and Oracle 23ai | 10% | - OCI Generative AI Service features
|
| Topic 2: Generative AI and Large Language Models | 15% | - Generative AI concepts
|
| Topic 3: Deep Learning Foundations | 15% | - Deep Learning and neural networks
|
| Topic 4: OCI AI Portfolio | 15% | - Overview of OCI AI offerings
|
| Topic 5: AI Foundations | 10% | - Artificial Intelligence basics and terminology
|
| Topic 6: Machine Learning Foundations | 15% | - Machine Learning fundamentals
|
| Topic 7: Introduction to OCI AI Services | 20% | - OCI AI Service APIs
|
Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions:
Question 1
What does " fine-tuning " refer to in the context of OCI Generative AI service?
A. Adjusting the model parameters to improve accuracy
B. Encrypting the data for security reasons
C. Doubling the neural network layers
D. Upgrading the hardware of the AI clusters
Question 2
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
A. Chat models
B. Generation models
C. Translation models
D. Embedding models
Question 3
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. Regression
B. Clustering
C. Binary Classification
D. Multi-Class Classification
Question 4
What would you use Oracle AI Vector Search for?
A. Manage database security protocols.
B. Store business data in a cloud database.
C. Query data based on keywords.
D. Query data based on semantics.
Question 5
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 focus on increasing the number of tokens while keeping the model size constant.
C. They disregard model size and prioritize high-quality data only.
D. They ensure that the model size, training time, and data size are balanced for optimal results.
Solutions:
| Question 1 Answer: A | Question 2 Answer: C | Question 3 Answer: D | Question 4 Answer: D | Question 5 Answer: D |





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