Oracle 1Z0-1127-25 Exam Dumps

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=

T-Few, a Parameter-Efficient Fine-Tuning method, updates fewer parameters than Vanilla fine-tuning, leading to faster training and lower computational costs---Option D is correct. Option A (complexity) isn't directly affected---structure remains. Option B (generalization) may occur but isn't the primary advantage. Option C (interpretability) isn't a focus. Efficiency is T-Few's hallmark.

: OCI 2025 Generative AI documentation likely compares T-Few and Vanilla under fine-tuning benefits.

Comprehensive and Detailed In-Depth Explanation=

Soft prompting (e.g., prompt tuning) involves adding trainable parameters (soft prompts) to an LLM's input while keeping the model's weights frozen, adapting it to tasks without task-specific retraining. This is efficient when fine-tuning or large datasets aren't feasible, making Option C correct. Option A suits full fine-tuning, not soft prompting, which avoids extensive labeled data needs. Option B could apply, but domain adaptation often requires more than soft prompting (e.g., fine-tuning). Option D describes continued pretraining, not soft prompting. Soft prompting excels in low-resource customization.

: OCI 2025 Generative AI documentation likely discusses soft prompting under parameter-efficient methods.

Comprehensive and Detailed In-Depth Explanation=

RAG enhances text generation by combining an LLM's internal knowledge with external data retrieved from sources (e.g., vector databases), improving accuracy and relevance. This makes Option B correct. Option A describes standalone LLMs, not RAG. Option C misrepresents RAG's purpose---data is used, not just stored. Option D is incorrect---RAG generates new text, not just retrieves. RAG is ideal for dynamic, informed responses.

: OCI 2025 Generative AI documentation likely explains RAG under advanced generation techniques.

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.