裸考拿下Oracle AI Foundations Associate认证
digoal约 4 分钟裸考拿下Oracle AI Foundations Associate认证
和MySQL免费认证一个尿性: 《MySQL认证免费了, 它的“阴谋”是什么?》
Oracle AI 认证也免费了, 考完告诉大家一下, 里面其实很多都是暗藏宣传自家OCI的内容, 自行甄别吧.
有了阿里云大模型(LLM) ACA ACP的认证基础, 考Oracle AI Foundations Associate认证非常轻松. 40道题60分钟, 裸考居然98分通过.
下面是阿里云 《大模型(LLM) ACA ACP 通关经验分享》 . ACA ACP考试辅导资料非常有价值, 建议学习.
Oracle AI 认证在Oracle Cloud Infrastructure (OCI) 体系里
https://www.oracle.com/cn/education/certification/#oci
搜索Oracle AI相关的认证
https://mylearn.oracle.com/ou/search/oracle-ai-vector-search-professional
发现有3个AI相关的认证, 其中第一个是免费考的, 地址也有点不太一样exam-unproctored下.
学习视频都免费, 就算不考也可以学习一下, 学到就是自己的.
1、Oracle Cloud Infrastructure 2025 AI Foundations Associate
免费课程:
https://mylearn.oracle.com/ou/course/oracle-cloud-infrastructure-ai-foundations/147805/223411
免费考试地址:
https://mylearn.oracle.com/ou/exam-unproctored/oracle-cloud-infrastructure-2025-ai-foundations-associate-1z0-1122-25/147781/241962
2、Oracle Cloud Infrastructure 2025 Generative AI Professional
免费课程:
https://mylearn.oracle.com/ou/learning-path/become-a-oci-generative-ai-professional/147863
https://mylearn.oracle.com/ou/course/oracle-cloud-infrastructure-generative-ai-professional/147932/243479
付费考试地址:
https://mylearn.oracle.com/ou/exam/oracle-cloud-infrastructure-2025-generative-ai-professional-1z0-1127-25/35644/147386/241953
https://education.oracle.com/products/trackp_OCI25GAIOCP
¥1,671
Format: Multiple Choice
Duration: 90 Minutes
Exam Price: ¥1,671
Number of Questions: 50
Passing Score: 68%
Validation: This exam has been validated against Oracle Cloud Infrastructure 2025
Policy: Cloud Recertification
考点
Fundamentals of Large Language Models (LLMs)
Explain the fundamentals of LLMs Understand LLM architectures Design and use prompts for LLMs Understand LLM fine-tuning Understand the fundamentals of code models, multi-modal, and language agents
Using OCI Generative AI Service
Explain the fundamentals of OCI Generative AI service Use pretranined foundational models for Chat and Embedding Create dedicated AI clusters for fine-tuning and inference Fine-tune base models with custom dataset Create and use model endpoints for inference Explore OCI Generative AI security architecture
Implement RAG using OCI Generative AI service
Explain OCI Generative AI integration with LangChain and Oracle Database 23ai Explain RAG and RAG workflow Discuss loading, splitting and chunking of documents for RAG Create embeddings of chunks using OCI Generative AI service Store and index embedded chunks in Oracle Database 23ai Describe similarity search and retrieve chunks from Oracle Database 23ai Explain response generation using OCI Generative AI service
Using OCI Generative AI RAG Agents service
Explain the fundamentals of OCI Generative AI Agents service Discuss options for creating knowledge bases Create and deploy agents using knowledge bases Invoke deployed RAG agent as a chatbot
3、Oracle AI Vector Search Professional
免费课程:
https://mylearn.oracle.com/ou/course/oracle-ai-vector-search-fundamentals/140188/223444
付费考试地址:
https://mylearn.oracle.com/ou/exam/oracle-ai-vector-search-professional-1z0-184-25/35644/144913/236030
https://education.oracle.com/ouexam-pexam_1z0-184-25/pexam_1Z0-184-25
¥1,671
Format: Multiple Choice
Duration: 90 Minutes
Exam Price: ¥1,671
Number of Questions: 50
Passing Score: 68%
Validation: This exam is valid for Oracle Database 23ai
考点:
The following table lists the exam objectives and their weightings.
Understand Vector Fundamentals
Use Vector Data type for storing embeddings and enabling semantic queries Use Vector Distance Functions and Metrics for AI vector search Perform DML Operations on Vectors Perform DDL Operations on Vectors
Using Vector Indexes
Create Vector Indexes to speed up AI vector search Use HNSW Vector Index for search queries Use IVF Vector Index for search queries
Performing Similarity Search
Perform Exact Similarity Search Perform approximate similarity search using Vector Indexes Perform Multi-Vector similarity search for multi-document search
Using Vector Embeddings
Generate Vector Embeddings outside the Oracle database Generate Vector Embeddings inside the Oracle database Store Vector Embeddings in Oracle database
Building a RAG Application
Understand Retrieval-augmented generation (RAG) concepts Create a RAG application using PL/SQL Create a RAG application using Python
Leveraging related AI capabilities
Use Exadata AI Storage to accelerate AI vector search Use Select AI with Autonomous to query data using natural language prompts Use SQL Loader for loading vector data Use Oracle Data Pump for loading and unloading vector data