THE BEST ORACLE - 1Z0-1122-24 - ORACLE CLOUD INFRASTRUCTURE 2024 AI FOUNDATIONS ASSOCIATE RELIABLE TEST SIMS

The Best Oracle - 1z0-1122-24 - Oracle Cloud Infrastructure 2024 AI Foundations Associate Reliable Test Sims

The Best Oracle - 1z0-1122-24 - Oracle Cloud Infrastructure 2024 AI Foundations Associate Reliable Test Sims

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Oracle 1z0-1122-24 Exam Syllabus Topics:

TopicDetails
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
  • 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.
Topic 3
  • 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 4
  • 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 5
  • Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
Topic 6
  • Intro to DL Foundations: This section covers Deep Learning (DL) is a subset of ML that focuses on neural networks with many layers, and understanding its core concepts is vital for working with complex models.

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q33-Q38):

NEW QUESTION # 33
What is the benefit of using embedding models in OCI Generative AI service?

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

Answer: C

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 # 34
Which capability is supported by the Oracle Cloud Infrastructure Vision service?

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

Answer: C

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 # 35
What is the purpose of the model catalog in OCI Data Science?

  • A. To store, track, share, and manage models
  • B. To provide a preinstalled open source library
  • C. To create and switch between different environments
  • D. To deploy models as HTTP endpoints

Answer: A

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 # 36
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?

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

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

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

Answer: A

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
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