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Oracle 1z0-1122-24 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
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 AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
Topic 4	Intro to OCI AI Services: This section is about exploring OCI AI Services and their related APIs, such as those for Language, Vision, Document Understanding, and Speech, which are essential for developers and businesses looking to integrate AI into their operations.

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q35-Q40):

NEW QUESTION # 35
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?

A. It converts audio files into text.
B. It provides real-time translation of text.
C. It recognizes and extracts text from a document.
D. It enhances the visual quality of documents.

Answer: C

Explanation:
The Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure (OCI) Document Understanding recognizes and extracts text from documents. This capability is fundamental for converting printed or handwritten text into a machine-readable format, allowing for further processing, such as text analysis, search, and archiving. OCI's OCR is an essential tool in automating document processing workflows, enabling businesses to digitize and manage their documents efficiently.

NEW QUESTION # 36
What does "fine-tuning" refer to in the context of OCI Generative AI service?

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

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 # 37
What feature of OCI Data Science provides an interactive coding environment for building and training models?

A. Conda environment
B. Notebook sessions
C. Model catalog
D. Accelerated Data Science (ADS) SDK

Answer: B

Explanation:
In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.

NEW QUESTION # 38
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. Training a model on a diverse range of tasks
D. Teaching a model through zero-shot learning

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 # 39
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

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

Answer: C

NEW QUESTION # 40
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