Get 1z0-1127-25 Dumps : Verified 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:

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.

Comprehensive and Detailed In-Depth Explanation:

Cosine distance measures the angle between two vectors, where 0 means the vectors point in the same direction (cosine similarity = 1), indicating high similarity in embeddings’ semantic content—Option C is correct. Option A (dissimilar) aligns with a distance of 1. Option B is vague—directional similarity matters. Option D (magnitude) isn’t relevant—cosine ignores magnitude. This is key for semantic comparison.

OCI 2025 Generative AI documentation likely explains cosine distance under vector database metrics.

Comprehensive and Detailed In-Depth Explanation:

Dot Product computes the raw similarity between two vectors, factoring in both magnitude and direction, while Cosine Distance (or similarity) normalizes for magnitude, focusing solely on directional alignment (angle), making Option C correct. Option A is vague—both measure similarity, not distinct content vs. topicality. Option B is false—both address semantics, not syntax. Option D is incorrect—neither measures word overlap or style directly; they operate on embeddings. Cosine is preferred for normalized semantic comparison.

OCI 2025 Generative AI documentation likely explains these metrics under vector similarity in embeddings.

Comprehensive and Detailed In-Depth Explanation:

In OCI, fine-tuned models are stored in Object Storage, encrypted by default, ensuring privacy and security per cloud best practices—Option B is correct. Option A (shared) violates privacy. Option C (unencrypted) contradicts security standards. Option D (Key Management) stores keys, not models. Encryption protects customer data.

OCI 2025 Generative AI documentation likely details storage security under fine-tuning workflows.