1z0-1127-25 Exam Dumps - Oracle Cloud Infrastructure Questions and Answers

Comprehensive and Detailed In-Depth Explanation:

LangSmith Evaluators assess LLM outputs for qualities like coherence (A), factual accuracy (C), and bias/toxicity (D), aiding development and debugging. Aligning code readability (B) pertains to software engineering, not LLM evaluation, making it the odd one out—Option B is correct as NOT a use case. Options A, C, and D align with LangSmith’s focus on text quality and ethics.

OCI 2025 Generative AI documentation likely lists LangSmith Evaluator use cases under evaluation tools.

Comprehensive and Detailed In-Depth Explanation:

Vector databases store data as high-dimensional vectors (embeddings) and are optimized for similarity searches using metrics like cosine distance, unlike relational databases, which use tabular rows and columns for structured data. This makes Option D correct. Options A and C describerelational databases, not vector ones. Option B is false, as vector databases are specifically designed for high-dimensional spaces. Vector databases excel in semantic search and LLM integration.

OCI 2025 Generative AI documentation likely contrasts vector and relational databases under data storage.

Comprehensive and Detailed In-Depth Explanation:

Prompt injection (jailbreaking) attempts to bypass an LLM’s restrictions by crafting prompts that trick it into revealing restricted information or behavior. Option A asks the model to creatively circumvent its protocols, a classic jailbreaking tactic—making it correct. Option B is a hypothetical persuasion task, not a bypass. Option C tests privacy handling, not injection. Option D is a creative writing prompt, not an attempt to break rules. A seeks to exploit protocol gaps.

OCI 2025 Generative AI documentation likely addresses prompt injection under security or ethics sections.

Comprehensive and Detailed In-Depth Explanation:

Prompt templates are predefined, reusable structures (e.g., with placeholders for variables) that guide LLM prompt creation, streamlining consistent input formatting. This makes Option B correct. Option A is false, as templates aren’t complex algorithms but simple frameworks. Option C is incorrect, as templates are customizable. Option D is wrong, as they handle text, not just numbers.Templates enhance efficiency in prompt engineering.

OCI 2025 Generative AI documentation likely covers prompt templates under prompt engineering or LangChain tools.

Here is the next batch of 10 questions (21–30) from your list, formatted as requested with detailed explanations. The answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity.

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:

In LangChain, a chain interacts with memory after receiving user input (to load prior context) but before execution (to inform the process), and again after the core logic (to update memory with new context) but before the final output. This ensures context continuity, making Option C correct. Option A is too late, missing pre-execution context. Option B is misordered. Option D overstates interaction, as it’s not continuous but at specific points. Memory integration is key for stateful chains.

OCI 2025 Generative AI documentation likely details memory interaction under LangChain workflows.