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Last Updated: Sep 13, 2026
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Oracle 1Z0-1127-25 Exam Overview:
| Certification Vendor: | Oracle |
|---|---|
| Exam Name: | Oracle Cloud Infrastructure 2025 Generative AI Professional |
| Exam Number: | 1Z0-1127-25 |
| Related Certifications: | Oracle Cloud Infrastructure Architect Associate Oracle Cloud Infrastructure Data Science Professional Oracle Cloud Infrastructure Foundations |
| Certificate Validity Period: | Typically 2 years (Oracle certification policies may vary) |
| Real Exam Qty: | 50-60 |
| Exam Price: | USD 245 |
| Available Languages: | English |
| Exam Format: | Multiple Response, Multiple Choice |
| Exam Duration: | 90 minutes |
| Recommended Training: | Oracle University OCI Generative AI Training |
| Exam Registration: | Oracle Certification Portal |
| Sample Questions: | Oracle 1Z0-1127-25 Sample Questions |
| Exam Way: | Online proctored or test center delivery via Oracle certification platform |
| Pre Condition: | Recommended: basic understanding of cloud computing, machine learning concepts, and Oracle Cloud Infrastructure services |
| Official Syllabus URL: | https://education.oracle.com/ |
Oracle 1Z0-1127-25 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: OCI Generative AI Service | - Model selection and deployment options in OCI - OCI Generative AI service capabilities and architecture |
| Topic 2: Prompt Engineering and Optimization | - Controlling model output and tuning prompts - Prompt design techniques |
| Topic 3: Retrieval-Augmented Generation (RAG) | - Knowledge grounding and contextual retrieval - Vector databases and embeddings usage |
| Topic 4: Model Customization and Fine-Tuning | - Evaluating and improving model performance - Fine-tuning concepts and workflows |
| Topic 5: Security, Governance, and Responsible AI | - Responsible AI principles and risk mitigation - Data privacy and model security in OCI |
| Topic 6: Generative AI Fundamentals | - Foundations of Generative AI and Large Language Models (LLMs) - Core concepts: tokens, embeddings, transformers |
| Topic 7: Deployment and Operations | - Integration of Generative AI into applications - Monitoring and lifecycle management |
Your Oracle 1Z0-1127-25 Exam Questions, Answered
The Oracle 1Z0-1127-25 exam, officially titled Oracle Cloud Infrastructure 2025 Generative AI Professional, is the required test for earning the Oracle Cloud Infrastructure (OCI) Generative AI Professional certification, a credential at the Professional level. Passing it validates the skills Oracle expects from certified professionals, and it can also support progress toward related credentials such as Oracle Cloud Infrastructure Foundations, Oracle Cloud Infrastructure Architect Associate, Oracle Cloud Infrastructure Data Science Professional.
The 1Z0-1127-25 exam includes 50-60 questions, and you have 90 minutes to complete it. Before exam day, divide the available time by the question count so you know the pace you need to hold, and practice flagging time-consuming items for review instead of stalling on a single question. Timed sessions in the Actualtests4sure test engine are the easiest way to build that rhythm before it counts.
Oracle asks candidates to meet the following requirement before registering: Recommended: basic understanding of cloud computing, machine learning concepts, and Oracle Cloud Infrastructure services. Exam policies do change, so confirm the latest details on the official exam page at https://education.oracle.com/ before you book.
You can book your seat through the official registration channels below:
The 1Z0-1127-25 exam is delivered in the following format: Online proctored or test center delivery via Oracle certification platform.
Oracle recommends the following official training for this exam:
A course builds the theory; practice turns it into exam-day performance. Once you finish a class, the 90 practice questions from Actualtests4sure show you how the same knowledge appears in exam-style items.
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The Oracle Cloud Infrastructure 2025 Generative AI Professional exam is organized into 7 major domains. Some of the key domains include:
- Deployment and Operations
- Prompt Engineering and Optimization
- OCI Generative AI Service
Scroll up to the Exam Topics section for the complete breakdown, and use it to plan how much study time each domain deserves.
Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions:
What is LangChain?
- A. A Java library for text summarization
- B. A JavaScript library for natural language processing
- C. A Ruby library for text generation
- D. A Python library for building applications with Large Language Models
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What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?
- A. It controls the randomness of the model's output, affecting its creativity.
- B. It assigns a penalty to frequently occurring tokens to reduce repetitive text.
- C. It specifies a string that tells the model to stop generating more content.
- D. It determines the maximum number of tokens the model can generate per response.
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How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?
- A. Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance.
- B. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.
- C. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.
- D. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.
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Which statement best describes the role of encoder and decoder models in natural language processing?
- A. Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.
- B. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.
- C. Encoder models and decoder models both convert sequences of words into vector representations without generating new text.
- D. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to generate a sequence of words.
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Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?
- A. Retriever
- B. Generator
- C. Encoder-Decoder
- D. Ranker
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