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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q40-Q45):

NEW QUESTION # 40
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 ensure that the model size, training time, and data size are balanced for optimal results.
C. They focus on increasing the number of tokens while keeping the model size constant.
D. They disregard model size and prioritize high-quality data only.

Answer: B

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 # 41
Which feature of OCI Speech helps make transcriptions easier to read and understand?

A. Profanity filtering
B. Audio tuning
C. Text normalization
D. Timestamping

Answer: C

Explanation:
The text normalization feature of OCI Speech helps make transcriptions easier to read and understand by converting spoken language into a more standardized and grammatically correct format. This process includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear, accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions, making them more accessible and easier to process in downstream applications.
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NEW QUESTION # 42
You are working on a multilingual public announcement system. Which AI task will you use to implement it?

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

Answer: D

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 # 43
What is the primary benefit of using the OCI Language service for text analysis?

A. It provides image processing capabilities.
B. It allows for text analysis at scale without machine learning expertise.
C. It only works with structured data.
D. It requires extensive machine learning expertise to use.

Answer: B

Explanation:
The primary benefit of using the OCI Language service for text analysis is its ability to scale text analysis without requiring users to have extensive machine learning expertise. The service abstracts the complexities of machine learning, allowing businesses to easily process and analyze large amounts of text data through pre-built models. This accessibility makes it possible for a broader range of users to leverage advanced text analysis capabilities, facilitating insights from textual data without needing to develop and train models from scratch.

NEW QUESTION # 44
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. Both involve retraining the model, but Prompt Engineering does it more often.
C. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
D. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.

Answer: C

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 # 45
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