1Z0-1122-24 AUTHENTIC EXAM QUESTIONS & DUMPS 1Z0-1122-24 GUIDE

1z0-1122-24 Authentic Exam Questions & Dumps 1z0-1122-24 Guide

1z0-1122-24 Authentic Exam Questions & Dumps 1z0-1122-24 Guide

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Oracle 1z0-1122-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact 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 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 4
  • 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 5
  • 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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The Oracle 1z0-1122-24 Exam Questions give you a complete insight into each chapter and an easy understanding with simple and quick-to-understand language. The Oracle 1z0-1122-24 exam dumps are the best choice to make. The common problem Oracle 1z0-1122-24 Exam applicants face is seeking updated and real Oracle 1z0-1122-24 practice test questions to prepare successfully for the cherished Oracle Cloud Infrastructure 2024 AI Foundations Associate 1z0-1122-24 certification exam.

Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q21-Q26):

NEW QUESTION # 21
Which is NOT a capability of OCI Vision's image analysis?

  • A. Assigning classification labels to images
  • B. Locating and extracting text in images
  • C. Translating text in images to another language
  • D. Object detection with bounding boxes

Answer: C

Explanation:
OCI Vision's image analysis capabilities include locating and extracting text from images, assigning classification labels to images, and detecting objects with bounding boxes. However, translating text in images to another language is not a capability of OCI Vision's image analysis. This functionality typically requires an additional layer of processing, such as integration with a language translation service, which is beyond the scope of OCI Vision's core image analysis features.
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NEW QUESTION # 22
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

  • A. They ensure that the model size, training time, and data size are balanced for optimal results.
  • B. They disregard model size and prioritize high-quality data only.
  • C. They focus on increasing the number of tokens while keeping the model size constant.
  • D. They prioritize larger model sizes to achieve better performance.

Answer: A

Explanation:
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.


NEW QUESTION # 23
You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?

  • A. Speech
  • B. Language
  • C. Vision
  • D. Document Understanding

Answer: A

Explanation:
For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the "Speech" service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.


NEW QUESTION # 24
What is "in-context learning" in the realm of Large Language Models (LLMs)?

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

Answer: D

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 # 25
Which capability is supported by the Oracle Cloud Infrastructure Vision service?

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

Answer: D

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 # 26
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