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Oracle 1z0-1122-24 Exam Syllabus Topics:

Topic	Details
Topic 1	Get Started with OCI AI Portfolio: This section is about the OCI AI Portfolio which offers a comprehensive suite of services and infrastructure for developing and deploying AI models. Exploring the overview of OCI AI Services provides insight into the tools available for AI development.
Topic 2	OCI Generative AI and Oracle 23ai: This section covers CI Generative AI Services that are a key component of Oracle's AI offerings, and exploring these services provides a clear understanding of how Oracle supports generative AI applications.
Topic 3	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 4	Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
Topic 5	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.

Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q34-Q39):

NEW QUESTION # 34
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. Training a model on a diverse range of tasks
C. Modifying the behavior of a pretrained LLM permanently
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 # 35
What is the benefit of using embedding models in OCI Generative AI service?

A. They optimize the use of computational resources.
B. They facilitate semantic searches.
C. They simplify managing databases.
D. They enable creating detailed graphics.

Answer: B

Explanation:
Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for "financial reports," an embedding model can understand that "quarterly earnings" is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .

NEW QUESTION # 36
What would you use Oracle AI Vector Search for?

A. Query data based on keywords.
B. Manage database security protocols.
C. Store business data in a cloud database.
D. Query data based on semantics.

Answer: D

Explanation:
Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .

NEW QUESTION # 37
Which AI domain can be employed for identifying patterns in images and extract relevant features?

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

Answer: C

Explanation:
Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.

NEW QUESTION # 38
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

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

Answer: A

NEW QUESTION # 39
......

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