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Oracle 1Z0-1122-25 Exam Syllabus Topics:

Topic	Details
Topic 1	Intro to ML Foundations: This section evaluates the knowledge of Machine Learning Engineers in understanding machine learning principles and methodologies. It explores the basics of supervised learning, focusing on regression and classification techniques, along with unsupervised learning methods such as clustering and anomaly detection. It also introduces reinforcement learning fundamentals, helping professionals grasp the different approaches used to train AI models.
Topic 2	OCI Generative AI and Oracle 23ai: This section evaluates the skills of Cloud AI Architects in utilizing Oracle’s generative AI capabilities. It includes a deep dive into OCI Generative AI services, Autonomous Database Select AI for enhanced data intelligence and Oracle Vector Search for efficient information retrieval in AI-driven applications.
Topic 3	Intro to AI Foundations: This section of the exam measures the skills of AI Practitioners and Data Analysts in understanding the fundamentals of artificial intelligence. It covers key concepts, AI applications across industries, and the types of data used in AI models. It also explains the differences between artificial intelligence, machine learning, and deep learning, providing clarity on how these technologies interact and complement each other.
Topic 4	Intro to OCI AI Services: This section tests the expertise of AI Solutions Engineers in working with OCI AI services and related APIs. It provides insights into key AI services such as language processing, computer vision, document understanding, and speech recognition, allowing professionals to leverage Oracle’s AI ecosystem for building intelligent applications.
Topic 5	Intro to Generative AI & LLMs: This section tests the abilities of AI Developers to understand generative AI and large language models. It introduces the principles of generative AI, explains the fundamentals of large language models (LLMs), and discusses the core workings of transformers, prompt engineering, instruction tuning, and LLM fine-tuning for optimizing AI-generated content.
Topic 6	Get started with OCI AI Portfolio: This section measures the proficiency of Cloud AI Specialists in exploring Oracle Cloud Infrastructure (OCI) AI services. It provides an overview of OCI AI and machine learning services, details AI infrastructure capabilities and explains responsible AI principles to ensure ethical and transparent AI development.

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q26-Q31):

NEW QUESTION # 26
What is the benefit of using embedding models in OCI Generative AI service?

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

Answer: A

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 # 27
What is "in-context learning" in the realm of Large Language Models (LLMs)?

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

Answer: B

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 # 28
What is the primary purpose of reinforcement learning?

A. Identifying patterns in data
B. Finding relationships within data sets
C. Learning from outcomes to make decisions
D. Making predictions from labeled data

Answer: C

Explanation:
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.

NEW QUESTION # 29
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

A. Reinforcement learning
B. Unsupervised learning
C. Supervised learning
D. Active learning

Answer: B

Explanation:
Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .

NEW QUESTION # 30
Which capability is supported by the Oracle Cloud Infrastructure Vision service?

A. Detecting and preventing fraud in financial transactions
B. Detecting vehicle number plates to issue speed citations
C. Analyzing historical data for unusual patterns
D. Generating realistic images from text

Answer: B

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 # 31
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1Z0-1122-25 BEST VCE, VALID DUMPS 1Z0-1122-25 PPT

1Z0-1122-25 Best Vce, Valid Dumps 1Z0-1122-25 Ppt