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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning Foundations | 15% | - Machine Learning fundamentals
|
| Topic 2: AI Foundations | 10% | - Artificial Intelligence basics and terminology
|
| Topic 3: OCI Generative AI and Oracle 23ai | 10% | - OCI Generative AI Service features
|
| Topic 4: Introduction to OCI AI Services | 20% | - OCI AI Service APIs
|
| Topic 5: OCI AI Portfolio | 15% | - Overview of OCI AI offerings
|
| Topic 6: Deep Learning Foundations | 15% | - Deep Learning and neural networks
|
| Topic 7: Generative AI and Large Language Models | 15% | - Generative AI concepts
|
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NEW QUESTION # 35
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
Answer: C
Explanation:
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.
NEW QUESTION # 36
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
Answer: D
Explanation:
The Oracle Cloud Infrastructure (OCI) Vision service is designed for image analysis tasks, which includes the capability to detect and recognize objects, such as vehicle number plates. This functionality is particularly useful for applications such as automated enforcement of traffic laws, where the system can identify vehicles exceeding speed limits and issue citations based on the detected number plates. This capability leverages advanced computer vision techniques to process and analyze visual data, making it suitable for applications in public safety, transportation, and law enforcement.
NEW QUESTION # 37
What is the purpose of the model catalog in OCI Data Science?
Answer: C
Explanation:
The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage machine learning models. This functionality is essential for maintaining an organized repository where data scientists and developers can collaborate on models, monitor their performance, and manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most recent and effective models are available for deployment. This capability is crucial in a collaborative environment where multiple stakeholders need access to the latest model versions for testing, evaluation, and deployment.
NEW QUESTION # 38
What does " fine-tuning " refer to in the context of OCI Generative AI service?
Answer: A
Explanation:
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.
NEW QUESTION # 39
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?
Answer: C
Explanation:
In this healthcare scenario, where the goal is to classify patients into three categories-Low Risk, Moderate Risk, and High Risk-based on their medical history and vital signs, a Multi-Class Classification algorithm is required. Multi-class classification is a type of supervised learning algorithm used when there are three or more classes or categories to predict. This method is well-suited for situations where each instance needs to be classified into one of several categories, which aligns with the requirement to categorize patients into different risk levels.
NEW QUESTION # 40
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