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First-Grade Oracle 1z0-1127-24: New Oracle Cloud Infrastructure 2024 Generative AI Professional Test Voucher - Pass-Sure Prep4sureExam 1z0-1127-24 Latest Exam Pdf

First-Grade Oracle 1z0-1127-24: New Oracle Cloud Infrastructure 2024 Generative AI Professional Test Voucher - Pass-Sure Prep4sureExam 1z0-1127-24 Latest Exam Pdf

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q19-Q24):

NEW QUESTION # 19
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: A


NEW QUESTION # 20
What does a cosine distance of 0 indicate about the relationship between two embeddings?

  • A. They are similar in direction
  • B. They have the same magnitude
  • C. They are completely dissimilar
  • D. They are unrelated

Answer: A


NEW QUESTION # 21
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?
The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

  • A. The improvement in accuracy achieved by the model during training on the user-uploaded data set
  • B. The level of incorrectness in the models predictions, with lower values indicating better performance
  • C. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
  • D. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

Answer: B

Explanation:
In the evaluation of OCI Generative AI fine-tuned models, "Loss" measures the level of incorrectness in the model's predictions. It quantifies how far the model's predictions are from the actual values. Lower loss values indicate better performance, as they reflect a smaller discrepancy between the predicted and true values. The goal during training is to minimize the loss, thereby improving the model's accuracy and reliability.
Reference
Articles on loss functions in machine learning
OCI Generative AI service documentation on model evaluation metrics


NEW QUESTION # 22
ow do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

  • A. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.
  • B. Dot Product measures the magnitude and direction vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.
  • C. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
  • D. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.

Answer: B


NEW QUESTION # 23
In LangChain, which retriever search type is used to balance between relevancy and diversity?

  • A. top k
  • B. similarity_score_threshold
  • C. mmr
  • D. similarity

Answer: C

Explanation:
In LangChain, the "mmr" (Maximal Marginal Relevance) search type is used to balance between relevancy and diversity when retrieving documents. This technique aims to select documents that are not only relevant to the query but also diverse from each other. This helps in avoiding redundancy and ensures that the retrieved set of documents covers a broader aspect of the topic.
Maximal Marginal Relevance (MMR) works by iteratively selecting documents that have high relevance to the query but low similarity to the documents already selected. This ensures that each new document adds new information and perspectives, rather than repeating what is already included.
Reference
LangChain documentation on retrievers and search types
Research papers and articles on Maximal Marginal Relevance (MMR)


NEW QUESTION # 24
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