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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: Machine Learning Foundations | 15% | - Machine Learning fundamentals
|
| Topic 3: AI Foundations | 10% | - Artificial Intelligence basics and terminology
|
| Topic 4: Generative AI and Large Language Models | 15% | - Generative AI concepts
|
| Topic 5: Introduction to OCI AI Services | 20% | - OCI AI Service APIs
|
| Topic 6: Deep Learning Foundations | 15% | - Deep Learning and neural networks
|
| Topic 7: OCI AI Portfolio | 15% | - Overview of OCI AI offerings
|
Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions:
Question #1
A customer feedback analysis team needs to extract insights from thousands of online product reviews. They want to determine whether customers express positive or negative opinions about different aspects of a product or service. Which feature of OCI Language is helpful?
A. Named entity recognition
B. Sentiment analysis
C. Language detection
D. Key phrase extraction
Question #2
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
A. Fairness
B. Respect for human autonomy
C. Prevention of harm
D. Explicability
Question #3
How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?
A. Both involve retraining the model, but Prompt Engineering does it more often.
B. Prompt Engineering modifies training data, while Fine-tuning alters the model ' s structure.
C. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
D. Prompt Engineering adjusts the model ' s parameters, while Fine-tuning crafts input prompts.
Question #4
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. Clustering
B. Multi-Class Classification
C. Binary Classification
D. Regression
Question #5
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 ensure that the model size, training time, and data size are balanced for optimal results.
C. They prioritize larger model sizes to achieve better performance.
D. They focus on increasing the number of tokens while keeping the model size constant.
Solutions:
| Question #1 Correct Answer: B | Question #2 Correct Answer: D | Question #3 Correct Answer: C | Question #4 Correct Answer: B | Question #5 Correct Answer: B |
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