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

Comprehensive and Detailed In-Depth Explanation:

Chain-of-Thought (CoT) prompting explicitly instructs an LLM to provide intermediate reasoning steps, enhancing complex task performance—Option B is correct. Option A (Step-Back) reframes problems, not emits steps. Option C (Least-to-Most) breaks tasks into subtasks, not necessarily showing reasoning. Option D (In-Context Learning) uses examples, not reasoning steps. CoT improves transparency and accuracy.

OCI 2025 Generative AI documentation likely covers CoT under advanced prompting techniques.

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:

Temperature adjusts the softmax distribution in decoding. Increasing it (e.g., to 2.0) flattens the curve, giving lower-probability words a better chance, thus increasing diversity—Option C is correct. Option A exaggerates—top words still have impact, just less dominance. Option B is backwards—decreasing temperature sharpens, not broadens. Option D is false—temperature directly alters distribution, not speed. This controls output creativity.

OCI 2025 Generative AI documentation likely reiterates temperature effects under decoding parameters.