Oracle 1Z0-1127-25 Exam Questions - Navigate Your Path to Success

The Oracle Cloud Infrastructure 2025 Generative AI Professional (1Z0-1127-25) exam is a good choice for Oracle Machine Learning/AI Engineers Gen AI Professionals and if the candidate manages to pass Oracle Cloud Infrastructure 2025 Generative AI Professional exam, he/she will earn Oracle Cloud , Oracle Cloud Infrastructure Certifications. Below are some essential facts for Oracle 1Z0-1127-25 exam candidates:

In actual Oracle Cloud Infrastructure 2025 Generative AI Professional (1Z0-1127-25) exam, a candidate can expect 50 Questions and the officially allowed time is expected to be around 90 Minutes.
TrendyCerts offers 88 Questions that are based on actual Oracle 1Z0-1127-25 syllabus.
Our Oracle 1Z0-1127-25 Exam Practice Questions were last updated on: Apr 15, 2025

Sample Questions for Oracle 1Z0-1127-25 Exam Preparation

Question 1

Why is normalization of vectors important before indexing in a hybrid search system?

AIt ensures that all vectors represent keywords only.

BIt significantly reduces the size of the database.

CIt standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.

DIt converts all sparse vectors to dense vectors.

Correct : C

Comprehensive and Detailed In-Depth Explanation=

Normalization scales vectors to unit length, ensuring comparisons (e.g., cosine similarity) reflect directional similarity, not magnitude differences, critical for hybrid search accuracy. This makes Option C correct. Option A is false---vectors represent semantics, not just keywords. Option B (size reduction) isn't the goal. Option D (sparse to dense) is unrelated---normalization adjusts length. Normalized vectors ensure fair similarity metrics.

: OCI 2025 Generative AI documentation likely explains normalization under vector preprocessing.

Options Selected by Other Users:

Question 2

An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?

AA diffusion model that specializes in producing complex outputs.

BA Large Language Model-based agent that focuses on generating textual responses

CA language model that operates on a token-by-token output basis

DA Retrieval Augmented Generation (RAG) model that uses text as input and output

Correct : A

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.