Oracle Updated 1z0-1127-25 Exam Questions and Answers by ryker

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:

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:

The task requires bidirectional text-image capabilities: analyzing images to generate text and generating images from text. Diffusion models (e.g., Stable Diffusion) excel at complex generative tasks, including text-to-image and image-to-text with appropriate extensions, making Option A correct. Option B (LLM) is text-only. Option C (token-based LLM) lacks image handling. Option D (RAG) focuses on text retrieval, not image generation. Diffusion models meet both needs.

OCI 2025 Generative AI documentation likely discusses diffusion models under multimodal applications.

Comprehensive and Detailed In-Depth Explanation:

Greedy decoding selects the word with the highest probability at each step, aiming for locally optimal choices without considering future tokens. This makes Option C correct. Option A (random selection) describes sampling, not greedy decoding. Option B (position-based) isn’t how greedy decoding works—it’s probability-driven. Option D (weighted random) aligns with top-k or top-p sampling, not greedy. Greedy decoding is fast but can lack diversity.

OCI 2025 Generative AI documentation likely explains greedy decoding under decoding strategies.