LEARNING 1Z0-1127-25 MATERIALS | 1Z0-1127-25 LATEST EXAM GUIDE

Learning 1Z0-1127-25 Materials | 1Z0-1127-25 Latest Exam Guide

Learning 1Z0-1127-25 Materials | 1Z0-1127-25 Latest Exam Guide

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q89-Q94):

NEW QUESTION # 89
Why is normalization of vectors important before indexing in a hybrid search system?

  • A. It ensures that all vectors represent keywords only.
  • B. It significantly reduces the size of the database.
  • C. It converts all sparse vectors to dense vectors.
  • D. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Normalization scales vectors to unit length, ensuring comparisons (e.g., cosine similarity) reflect directional similarity, not magnitude differences, critical for hybrid search accuracy. This makes Option C correct. Option A is false-vectors represent semantics, not just keywords. Option B (size reduction) isn't the goal. Option D (sparse to dense) is unrelated-normalization adjusts length. Normalized vectors ensure fair similarity metrics.
OCI 2025 Generative AI documentation likely explains normalization under vector preprocessing.


NEW QUESTION # 90
How does a presence penalty function in language model generation when using OCI Generative AI service?

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

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty in LLMs (including OCI's service) reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use. This discourages repetition, making Option D correct. Option A is false, as penalties depend on prior appearance, not uniform application. Option B is the opposite-penalizing unused tokens isn't the goal. Option C is incorrect, as the penalty isn't threshold-based (e.g., more than twice) but applied per reoccurrence. This enhances output diversity.
OCI 2025 Generative AI documentation likely details presence penalty under generation parameters.


NEW QUESTION # 91
How does a presence penalty function in language model generation?

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

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use, to discourage repetition. This makes Option D correct. Option A (equal penalties) ignores prior appearance. Option B is the opposite-penalizing unused tokens isn't the intent. Option C (more than twice) adds an arbitrary threshold not typically used. Presence penalty enhances output variety.OCI 2025 Generative AI documentation likely details presence penalty under generation control parameters.


NEW QUESTION # 92
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

  • A. "Top p" limits token selection based on the sum of their probabilities.
  • B. "Top p" selects tokens from the "Top k" tokens sorted by probability.
  • C. "Top p" assigns penalties to frequently occurring tokens.
  • D. "Top p" determines the maximum number of tokens per response.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
"Top p" (nucleus sampling) selects tokens whose cumulative probability exceeds a threshold (p), limiting the pool to the smallest set meeting this sum, enhancing diversity-Option C is correct. Option A confuses it with "Top k." Option B (penalties) is unrelated. Option D (max tokens) is a different parameter. Top p balances randomness and coherence.
OCI 2025 Generative AI documentation likely explains "Top p" under sampling methods.
Here is the next batch of 10 questions (81-90) from your list, formatted as requested with detailed explanations. The answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity.


NEW QUESTION # 93
What is the role of temperature in the decoding process of a Large Language Model (LLM)?

  • A. To determine the number of words to generate in a single decoding step
  • B. To increase the accuracy of the most likely word in the vocabulary
  • C. To decide to which part of speech the next word should belong
  • D. To adjust the sharpness of probability distribution over vocabulary when selecting the next word

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Temperature is a hyperparameter in the decoding process of LLMs that controls the randomness of word selection by modifying the probability distribution over the vocabulary. A lower temperature (e.g., 0.1) sharpens the distribution, making the model more likely to select the highest-probability words, resulting in more deterministic and focused outputs. A higher temperature (e.g., 2.0) flattens the distribution, increasing the likelihood of selecting less probable words, thus introducing more randomness and creativity. Option D accurately describes this role. Option A is incorrect because temperature doesn't directly increase accuracy but influences output diversity. Option B is unrelated, as temperature doesn't dictate the number of words generated. Option C is also incorrect, as part-of-speech decisions are not directly tied to temperature but to the model's learned patterns.
General LLM decoding principles, likely covered in OCI 2025 Generative AI documentation under decoding parameters like temperature.


NEW QUESTION # 94
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