1z0-1127-25 Exam Dumps : Oracle Cloud Infrastructure 2025 Generative AI Professional

Comprehensive and Detailed In-Depth Explanation:

Semantic search uses embeddings and NLP to understand the meaning, intent, and context behind a query, rather than just matching exact keywords (as in traditional search). This enables more relevant results, even if exact terms aren’t present, making Option C correct. Options A and B describe traditional keyword search mechanics. Option D is unrelated, as metadata like date or author isn’t the primary focus of semantic search. Semantic search leverages vector representations for deeper understanding.

OCI 2025 Generative AI documentation likely contrasts semantic and keyword search under search or retrieval sections.

Comprehensive and Detailed In-Depth Explanation:

T-Few fine-tuning enhances efficiency by updating only a small subset of transformer layers or parameters (e.g., via adapters), reducing computational load—Option D is correct. Option A (adding layers) increases complexity, not efficiency. Option B (all layers) describes Vanilla fine-tuning. Option C (excluding layers) is false—T-Few updates, not excludes. This selective approach optimizes resource use.

OCI 2025 Generative AI documentation likely details T-Few under PEFT methods.

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.