Oracle 1Z0-1127-25 Exam Dumps

Comprehensive and Detailed In-Depth Explanation=

Retrievers in LangChain fetch relevant information (e.g., documents, embeddings) from external knowledge bases (like vector stores) to provide context for LLM responses, especially in RAG setups. This makes Option B correct. Option A (training) is unrelated---Retrievers operate at inference. Option C (task breakdown) pertains to prompting techniques, not retrieval. Option D (pipeline combination) describes chains, not Retrievers specifically. Retrievers enhance context awareness.

: OCI 2025 Generative AI documentation likely defines Retrievers under LangChain components.

Comprehensive and Detailed In-Depth Explanation=

T-Few is ideal for smaller datasets (e.g., a few thousand samples) where full fine-tuning risks overfitting and is computationally wasteful---Option C is correct. Option A (semantic understanding) is too vague---dataset size matters more. Option B (dedicated cluster) isn't a condition for T-Few. Option D (large datasets) favors Vanilla fine-tuning. T-Few excels in low-data scenarios.

: OCI 2025 Generative AI documentation likely specifies T-Few use cases under fine-tuning guidelines.

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.

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.

Comprehensive and Detailed In-Depth Explanation=

OCI Generative AI typically offers pretrained models for summarization (A), generation (B), and embeddings (D), aligning with common generative tasks. Translation models (C) are less emphasized in generative AI services, often handled by specialized NLP platforms, making C the NOT category. While possible, translation isn't a core OCI generative focus based on standard offerings.

: OCI 2025 Generative AI documentation likely lists model categories under pretrained options.