Oracle 1Z0-1127-25 Exam Dumps

Comprehensive and Detailed In-Depth Explanation=

PEFT (e.g., LoRA, T-Few) updates a small subset of parameters (often new ones) using labeled, task-specific data, unlike classic fine-tuning, which updates all parameters---Option A is correct. Option B reverses PEFT's efficiency. Option C (no modification) fits soft prompting, not all PEFT. Option D (all parameters) mimics classic fine-tuning. PEFT reduces resource demands.

: OCI 2025 Generative AI documentation likely contrasts PEFT and fine-tuning under customization methods.

Comprehensive and Detailed In-Depth Explanation=

Embeddings in LLMs are high-dimensional vectors that encode the semantic meaning of words, phrases, or sentences, capturing relationships like similarity or context (e.g., 'cat' and 'kitten' being close in vector space). This allows the model to process and understand text numerically, making Option C correct. Option A is irrelevant, as embeddings don't deal with visual attributes. Option B is incorrect, as frequency is a statistical measure, not the purpose of embeddings. Option D is partially related but too narrow---embeddings capture semantics beyond just grammar.

: OCI 2025 Generative AI documentation likely discusses embeddings under data representation or vectorization topics.

Comprehensive and Detailed In-Depth Explanation=

In OCI, a dedicated AI cluster's usage is typically measured in unit hours, where 1 unit hour = 1 hour of cluster activity. For 10 days, assuming 24 hours per day, the calculation is: 10 days 24 hours/day = 240 hours. Thus, Option B (240 unit hours) is correct. Option A (480) might assume multiple clusters or higher rates, but the question specifies one cluster. Option C (744) approximates a month (31 days), not 10 days. Option D (20) is arbitrarily low.

: OCI 2025 Generative AI documentation likely specifies unit hour calculations under Dedicated AI Cluster pricing.

Comprehensive and Detailed In-Depth Explanation=

For a chatbot needing to answer policy queries (requiring up-to-date, specific data) and retain chat history (context awareness), an LLM with RAG is ideal. RAG integrates external data (e.g., policy documents) via retrieval and supports memory for session-long context, making Option B correct. Option A (keyword search) lacks reasoning and context retention. Option C (standalone LLM) can't dynamically fetch policy data. Option D (pre-trained LLM) is too vague and lacks RAG's capabilities. RAG meets both requirements effectively.

: OCI 2025 Generative AI documentation likely highlights RAG for dynamic, context-aware applications.

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.