VALID 1Z0-1122-24 EXAM VCE, 1Z0-1122-24 LATEST TEST VCE

Valid 1z0-1122-24 Exam Vce, 1z0-1122-24 Latest Test Vce

Valid 1z0-1122-24 Exam Vce, 1z0-1122-24 Latest Test Vce

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Tags: Valid 1z0-1122-24 Exam Vce, 1z0-1122-24 Latest Test Vce, Valid 1z0-1122-24 Exam Syllabus, 1z0-1122-24 Test Dumps Free, Reliable 1z0-1122-24 Exam Pattern

This format enables you to assess your 1z0-1122-24 test preparation with a Oracle 1z0-1122-24 certification exam. You can also customize your time and the kinds of Oracle 1z0-1122-24 Exam Questions of the Oracle 1z0-1122-24 practice test. ExamsLabs has formulated 1z0-1122-24 PDF questions for the convenience of Oracle 1z0-1122-24 test takers.

Oracle 1z0-1122-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 5
  • 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.

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q27-Q32):

NEW QUESTION # 27
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 # 28
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

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

Answer: D

Explanation:
Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans. Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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NEW QUESTION # 29
What is "in-context learning" in the realm of Large Language Models (LLMs)?

  • A. Teaching a model through zero-shot learning
  • B. Modifying the behavior of a pretrained LLM permanently
  • C. Training a model on a diverse range of tasks
  • 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 # 30
What is the purpose of the model catalog in OCI Data Science?

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

Answer: D

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 # 31
Which is NOT a capability of OCI Vision's image analysis?

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

Answer: D

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 # 32
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