100% PASS QUIZ MARVELOUS 1Z0-1122-25 LATEST ORACLE CLOUD INFRASTRUCTURE 2025 AI FOUNDATIONS ASSOCIATE EXAM QUESTIONS VCE

100% Pass Quiz Marvelous 1Z0-1122-25 Latest Oracle Cloud Infrastructure 2025 AI Foundations Associate Exam Questions Vce

100% Pass Quiz Marvelous 1Z0-1122-25 Latest Oracle Cloud Infrastructure 2025 AI Foundations Associate Exam Questions Vce

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q41-Q46):

NEW QUESTION # 41
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

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

Answer: C

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 # 42
What is "in-context learning" in the realm of Large Language Models (LLMs)?

  • A. Teaching a model through zero-shot learning
  • B. Providing a few examples of a target task via the input prompt
  • C. Modifying the behavior of a pretrained LLM permanently
  • 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 # 43
Which is NOT a capability of OCI Vision's image analysis?

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

Answer: A

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 # 44
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?

  • A. By analyzing sentiment in text documents
  • B. By transcribing spoken language
  • C. By generating lifelike speech from documents
  • D. By automating data extraction from documents

Answer: D

Explanation:
Explanation:


NEW QUESTION # 45
What is the difference between classification and regression in Supervised Machine Learning?

  • A. Classification assigns data points to categories, whereas regression predicts continuous values.
  • B. Classification and regression both predict continuous values.
  • C. Classification predicts continuous values, whereas regression assigns data points to categories.
  • D. Classification and regression both assign data points to categories.

Answer: A

Explanation:
In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either "spam" or "not spam." On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers "which category?" regression answers "how much?" or "what value?".


NEW QUESTION # 46
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