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Oracle 1z0-1122-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Introduction to OCI AI Services | 20% | - OCI AI Service APIs
|
| OCI Generative AI and Oracle 23ai | 10% | - OCI Generative AI Service features
|
| Machine Learning Foundations | 15% | - Machine Learning fundamentals
|
| AI Foundations | 10% | - Artificial Intelligence basics and terminology
|
| Generative AI and Large Language Models | 15% | - Generative AI concepts
|
| OCI AI Portfolio | 15% | - Overview of OCI AI offerings
|
| Deep Learning Foundations | 15% | - Deep Learning and neural networks
|
Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions:
Which is NOT a capability of OCI Vision ' s image analysis?
- A. Translating text in images to another language
- B. Object detection with bounding boxes
- C. Assigning classification labels to images
- D. Locating and extracting text in images
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What distinguishes Generative AI from other types of AI?
- A. Generative AI involves training models to perform tasks without human intervention.
- B. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
- C. Generative AI uses algorithms to predict outcomes based on past data.
- D. Generative AI focuses on making decisions based on user interactions.
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What feature of OCI Data Science provides an interactive coding environment for building and training models?
- A. Conda environment
- B. Model catalog
- C. Accelerated Data Science (ADS) SDK
- D. Notebook sessions
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What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?
- A. They are free of charge for all users.
- B. They provide high performance compute resources for fine-tuning tasks.
- C. They provide faster internet connection speeds.
- D. They allow access to unlimited database resources.
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What is the purpose of Attention Mechanism in Transformer architecture?
- A. Apply a specific function to each word individually.
- B. Break down a sentence into smaller pieces called tokens.
- C. Convert tokens into numerical forms (vectors) that the model can understand.
- D. Weigh the importance of different words within a sequence and understand the context.
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