PROVIDING YOU UNPARALLELED VALID 1Z0-1127-24 TEST PATTERN WITH 100% PASSING GUARANTEE

Providing You Unparalleled Valid 1z0-1127-24 Test Pattern with 100% Passing Guarantee

Providing You Unparalleled Valid 1z0-1127-24 Test Pattern with 100% Passing Guarantee

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q11-Q16):

NEW QUESTION # 11
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

  • A. By excluding transformer layers from the fine-tuning process entirely
  • B. By allowing updates across all layers of the model
  • C. By restricting updates to only a specific croup of transformer Layers
  • D. By incorporating additional layers to the base model

Answer: C

Explanation:
The utilization of T-Few transformer layers contributes to the efficiency of the fine-tuning process by restricting updates to only a specific group of transformer layers. This selective updating approach allows the model to adapt to new data without the need to retrain all layers, thus saving computational resources and time. By focusing on the most relevant parts of the model, T-Few fine-tuning achieves efficient and effective performance improvements.
Reference
Research papers on T-Few fine-tuning techniques
Technical guides on optimizing transformer models


NEW QUESTION # 12
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

  • A. The token will be the only one considered in the next generation step.
  • B. The token is less likely to follow the current token.
  • C. The token is unrelated to the current token and will not be used.
  • D. The token is more likely to follow the current token.

Answer: D

Explanation:
In the "Show Likelihoods" feature of language model token generation, a higher number assigned to a token indicates that the token is more likely to follow the current token. This likelihood is based on the model's probability distribution, where tokens with higher probabilities are considered more likely to be the next in the sequence. This feature helps in understanding the model's decision-making process and the relative probabilities of different tokens.
Reference
Technical documentation on language model token generation
Research articles on probability distributions in generative models


NEW QUESTION # 13
Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?

  • A. They are more expensive but provide higher quality data.
  • B. They increase the cost due to the need for real- time updates.
  • C. They offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.
  • D. They require frequent manual updates, which increase operational costs.

Answer: C

Explanation:
Using vector databases with Large Language Models (LLMs) offers cost-related benefits, particularly by providing real-time updated knowledge bases. This approach can be more cost-effective than fine-tuning LLMs frequently, as vector databases allow for the dynamic retrieval of information without the need for constant retraining. This reduces operational costs while maintaining access to up-to-date data.
Reference
Articles on the cost efficiency of vector databases
Research on integrating vector databases with LLMs for real-time updates


NEW QUESTION # 14
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 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.
  • 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 considers the sum of probabilities of the top tokens, whereas Top" selects from the Top k" tokens sorted by probability.
  • 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: A

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 # 15
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

  • A. Capacity to translate text in over u languages
  • B. Emphasis on syntactic clustering of word embedding's
  • C. Improved retrievals for Retrieval Augmented Generation (RAG) systems
  • D. Support for tokenizing longer sentences

Answer: C


NEW QUESTION # 16
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