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VCE 1Z0-1122-25 Dumps & 1Z0-1122-25 Exam Forum

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q36-Q41):

NEW QUESTION # 36
Which capability is supported by Oracle Cloud Infrastructure Language service?

A. Analyzing text to extract structured information like sentiment or entities
B. Detecting objects and scenes in images
C. Converting text into images
D. Translating text into speech

Answer: A

Explanation:
Oracle Cloud Infrastructure (OCI) Language service is specifically designed to analyze text and extract structured information such as sentiment, entities, key phrases, and language detection. This service provides natural language processing (NLP) capabilities that help users gain insights from unstructured text data. By identifying the sentiment (positive, negative, neutral) and recognizing entities (like names, dates, or places), the service enables businesses to process large volumes of text data efficiently, aiding in decision-making processes.

NEW QUESTION # 37
Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?

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

Answer: C

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 # 38
What is "in-context learning" in the realm of Large Language Models (LLMs)?

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

Answer: A

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 # 39
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?

A. They allow access to unlimited database resources.
B. They are free of charge for all users.
C. They provide high performance compute resources for fine-tuning tasks.
D. They provide faster internet connection speeds.

Answer: C

Explanation:
The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.

NEW QUESTION # 40
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?

A. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.
B. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.
C. Both involve retraining the model, but Prompt Engineering does it more often.
D. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.

Answer: D

Explanation:
In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model's internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
Fine-tuning, on the other hand, refers to the process of retraining a pretrained model on a smaller, task-specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively "specializing" the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.

NEW QUESTION # 41
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