1Z0-1122-24 LATEST TEST MATERIALS - 1Z0-1122-24 EXAM SIMULATOR ONLINE

1z0-1122-24 Latest Test Materials - 1z0-1122-24 Exam Simulator Online

1z0-1122-24 Latest Test Materials - 1z0-1122-24 Exam Simulator Online

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Oracle 1z0-1122-24 Exam Syllabus Topics:

TopicDetails
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
  • 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 AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
Topic 4
  • 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 5
  • 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.

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q11-Q16):

NEW QUESTION # 11
What is the purpose of the model catalog in OCI Data Science?

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

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 # 12
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

  • A. Generation models
  • B. Chat models
  • C. Embedding models
  • D. Translation models

Answer: D

Explanation:
The OCI Generative AI service offers various categories of pretrained foundational models, including Embedding models, Chat models, and Generation models. These models are designed to perform a wide range of tasks, such as generating text, answering questions, and providing contextual embeddings. However, Translation models, which are typically used for converting text from one language to another, are not a category available in the OCI Generative AI service's current offerings. The focus of the OCI Generative AI service is more aligned with tasks related to text generation, chat interactions, and embedding generation rather than direct language translation.


NEW QUESTION # 13
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

  • A. AI is a subset of DL, which is a subset of ML.
  • B. ML is a subset of AI, and DL is a subset of ML.
  • C. DL is a subset of AI, and ML is a subset of DL.
  • D. AI, ML, and DL are entirely separate fields with no overlap.

Answer: B

Explanation:
Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence "deep").
In this hierarchy:
AI includes all methods to make machines intelligent.
ML refers to the methods within AI that focus on learning from data.
DL is a specialized field within ML that deals with deep neural networks.


NEW QUESTION # 14
You are working on a multilingual public announcement system. Which AI task will you use to implement it?

  • A. Text to speech
  • B. Text summarization
  • C. Speech recognition
  • D. Audio recording

Answer: A

Explanation:
For a multilingual public announcement system, the AI task that would be most relevant is "Text to Speech" (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .


NEW QUESTION # 15
Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?

  • A. Language Detection
  • B. Text Classification
  • C. Sentiment Analysis
  • D. Text Generation

Answer: D

Explanation:
The OCI Language service offers several pretrained language processing capabilities, including Text Classification, Sentiment Analysis, and Language Detection. However, it does not natively support Text Generation as a part of its core language processing capabilities. Text Generation typically involves creating new content based on input prompts, which is a feature more commonly associated with models specifically designed for natural language generation.


NEW QUESTION # 16
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