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Pass Guaranteed Oracle First-grade 1z0-1122-24 - New Oracle Cloud Infrastructure 2024 AI Foundations Associate Test Sims

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Oracle 1z0-1122-24 Exam Syllabus Topics:

Topic
Details

Topic 1

Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.

Topic 2

Intro to ML Foundations: This section covers Machine Learning (ML) which is a critical area within AI, and understanding its fundamentals is crucial for anyone interested in this field. The section covers delving into the basics of ML allowing for a better grasp of how machines learn from data.

Topic 3

Intro to Generative AI & LLMs: This section is about covering generative AI which represents a powerful area of AI that involves creating new content or data. Exploring the overview of Generative AI helps in understanding its potential and applications.

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q29-Q34):

NEW QUESTION # 29
What is "in-context learning" in the realm of Large Language Models (LLMs)?

A. Providing a few examples of a target task via the input prompt
B. Modifying the behavior of a pretrained LLM permanently
C. Teaching a model through zero-shot learning
D. Training a model on a diverse range of tasks

Answer: A

Explanation:
"In-context learning" in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt. This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.

NEW QUESTION # 30
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

A. Prevention of harm
B. Explicability
C. Respect for human autonomy
D. Fairness

Answer: B

Explanation:
Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans. Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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NEW QUESTION # 31
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. Regression
C. Multi-Class Classification
D. Binary Classification

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 # 32
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?

A. Natural Language Processing
B. Computer Vision
C. Natural Language Processing
D. Anomaly Detection

Answer: C

Explanation:
Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.

NEW QUESTION # 33
What does "fine-tuning" refer to in the context of OCI Generative AI service?

A. Adjusting the model parameters to improve accuracy
B. Upgrading the hardware of the AI clusters
C. Doubling the neural network layers
D. Encrypting the data for security reasons

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 # 34
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