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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q52-Q57):

NEW QUESTION # 52
How does a presence penalty function in language model generation?

A. It penalizes a token each time it appears after the first occurrence.
B. It applies a penalty only if the token has appeared more than twice.
C. It penalizes only tokens that have never appeared in the text before.
D. It penalizes all tokens equally, regardless of how often they have appeared.

Answer: A

Explanation:
A presence penalty is a mechanism used in language model generation to discourage repetition of words or phrases in generated text. This is crucial for improving diversity in AI-generated responses.
How It Works:
The presence penalty increases the loss associated with words that have already appeared in the output.
The model is less likely to generate the same word multiple times, leading to more diverse responses.
Unlike frequency penalties, which increase with repeated occurrences, presence penalties apply as soon as a word appears.
Key Use Cases:
Avoiding redundant phrases in AI-generated text.
Enhancing creative writing applications where repetitive wording is undesirable.
Making chatbot conversations more engaging and natural.
🔹 Oracle Generative AI Reference:
Oracle's generative AI models implement presence and frequency penalties as part of their fine-tuning and model inference processes to balance text coherence and diversity.

NEW QUESTION # 53
Which statement describes the difference between Top V and Top p" in selecting the next token in the OCI Generative AI Generation models?

A. Top K considers the sum of probabilities of the top tokens, whereas Top" selects from the Top k" tokens sorted by probability.
B. Top k and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.
C. Top k selects the next token based on its position in the list of probable tokens, whereas "Top p" selects based on the cumulative probability of the Top token.
D. Top k and Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.

Answer: C

Explanation:
The difference between "Top k" and "Top p" in selecting the next token in generative models lies in their selection criteria:
Top k: This method selects the next token from the top k tokens based on their probability scores. It restricts the selection to a fixed number of the most probable tokens, irrespective of their cumulative probability.
Top p: Also known as nucleus sampling, this method selects tokens based on the cumulative probability until it exceeds a certain threshold p. It dynamically adjusts the number of tokens considered, ensuring that the sum of their probabilities meets or exceeds the specified p value. This allows for a more flexible and often more diverse selection compared to Top k.
Reference
Research articles on sampling techniques in language models
Technical documentation for generative AI models in OCI

NEW QUESTION # 54
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

A. Generator
B. Encoder-decoder
C. Retriever
D. Ranker

Answer: D

Explanation:
In Retrieval-Augmented Generation (RAG), the component responsible for evaluating and prioritizing the information retrieved by the retrieval system is the Ranker. After the Retriever fetches relevant documents or passages, the Ranker assesses these retrieved items based on their relevance to the query. It then prioritizes them, typically scoring and ordering the documents so that the most pertinent information is considered first in the generation process. This ensures that the generated response is based on the most relevant and useful content available.
Reference
Research papers on RAG (Retrieval-Augmented Generation)
Technical documentation on the architecture of RAG models

NEW QUESTION # 55
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?

A. A language model that operates on a token-by-token output basis
B. A Large Language Model based agent that focuses on generating textual responses
C. A diffusion model that specializes in producing complex outputs.
D. A Retrieval Augmented Generation (RAG) model that uses text as input and output

Answer: D

NEW QUESTION # 56
Which statement best describes the role of encoder and decoder models in natural language processing?

A. Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.
B. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.
C. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.
D. Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.

Answer: B

NEW QUESTION # 57
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Ray Knight Ray Knight

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