Important Oracle 1Z0-1127-25 Exam Questions

Answer: B

Explanation:

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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.

Answer: C

Explanation:

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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.

Answer: C

Explanation:

Comprehensive and Detailed In-Depth Explanation=

LCEL (LangChain Expression Language) is a declarative syntax in LangChain for composing chains---sequences of operations involving LLMs, tools, and memory. It simplifies chain creation with a readable, modular approach, making Option C correct. Option A is false, as LCEL isn't fordocumentation. Option B is incorrect, as LCEL is current, not legacy. Option D is wrong, as LCEL is part of LangChain, not a standalone LLM library. LCEL enhances flexibility in application design.

: OCI 2025 Generative AI documentation likely mentions LCEL under LangChain integration or chain composition.

Answer: D

Explanation:

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.

Answer: D

Explanation:

Comprehensive and Detailed In-Depth Explanation=

Chain-of-Thought (CoT) prompting encourages an LLM to emit intermediate reasoning steps before providing a final answer, improving performance on complex tasks by mimicking human reasoning. This matches the scenario, making Option D correct. Option A (In-context Learning) involves learning from examples in the prompt, not necessarily reasoning steps. Option B (Step-Back Prompting) involves reframing the problem, not emitting steps. Option C (Least-to-Most Prompting) breaks tasks into subtasks but doesn't focus on intermediate reasoning explicitly. CoT is widely recognized for reasoning tasks.

: OCI 2025 Generative AI documentation likely covers Chain-of-Thought under advanced prompting techniques.