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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q33-Q38):

NEW QUESTION # 33
Which feature of OCI Speech helps make transcriptions easier to read and understand?

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

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 # 34
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?

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

Answer: A

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 # 35
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 # 36
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?

A. Backpropagation
B. Gradient Descent
C. Random Forest
D. Support Vector Machine

Answer: A

Explanation:
Backpropagation is the algorithm primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN). It is a supervised learning algorithm that calculates the gradient of the loss function with respect to each weight by applying the chain rule, propagating the error backward from the output layer to the input layer. This process updates the weights to minimize the error, thus improving the model's accuracy over time.
Gradient Descent is closely related as it is the optimization algorithm used to adjust the weights based on the gradients computed by backpropagation, but backpropagation is the specific method used to calculate these gradients.

NEW QUESTION # 37
What is "in-context learning" in the realm of Large Language Models (LLMs)?

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

Answer: C

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 # 38
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