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Oracle 1z0-1122-26 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| OCI Generative AI and Oracle Database AI Capabilities | - OCI Generative AI
|
| Generative AI and Large Language Models | - Large Language Models
|
| Oracle AI and Machine Learning Services | - OCI Machine Learning Services
|
| Artificial Intelligence and Machine Learning Fundamentals | - Machine Learning Fundamentals
|
Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions:
How does AI enhance human efforts?
- A. By increasing the physical strength of humans
- B. By deleting data humans need to handle
- C. By processing data at a speed and effectiveness far beyond human capability
- D. By completely replacing human workers in all tasks
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What is the benefit of using embedding models in OCI Generative AI service?
- A. They enable creating detailed graphics.
- B. They simplify managing databases.
- C. They facilitate semantic searches.
- D. They optimize the use of computational resources.
Explanation: Only visible for DumpsMaterials members. You can sign-up / login (it's free).
Which capability is supported by Oracle Cloud Infrastructure Language service?
- A. Detecting objects and scenes in images
- B. Translating text into speech
- C. Analyzing text to extract structured information like sentiment or entities
- D. Converting text into images
Explanation: Only visible for DumpsMaterials members. You can sign-up / login (it's free).
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
- A. They disregard model size and prioritize high-quality data only.
- B. They focus on increasing the number of tokens while keeping the model size constant.
- C. They prioritize larger model sizes to achieve better performance.
- D. They ensure that the model size, training time, and data size are balanced for optimal results.
What is " in-context learning " in the realm of Large Language Models (LLMs)?
- A. Modifying the behavior of a pretrained LLM permanently
- B. Teaching a model through zero-shot learning
- C. Training a model on a diverse range of tasks
- D. Providing a few examples of a target task via the input prompt
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