Oracle 1Z0-184-25 Exam Overview:
| Certification Vendor: | Oracle |
| Exam Name: | Oracle AI Vector Search Professional |
| Exam Number: | 1Z0-184-25 |
| Available Languages: | English |
| Passing Score: | 68% |
| Exam Duration: | 90 minutes |
| Exam Format: | Multiple Choice |
| Real Exam Qty: | 50 |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Oracle Database 23ai Certified Professional |
| Exam Price: | $245 USD |
| Sample Questions: | Oracle 1Z0-184-25 Sample Questions |
| Exam Way: | Online Proctored |
| Pre Condition: | Candidates should have basic familiarity with Python and AI/ML concepts. |
| Official Syllabus URL: | https://education.oracle.com/oracle-ai-vector-search-professional/pexam_1Z0-184-25 |
Oracle 1Z0-184-25 Exam Syllabus Topics:
| Topic | Details |
|---|
| Topic 1 | - Using Vector Embeddings: This section measures the abilities of AI Developers in generating and storing vector embeddings for AI applications. It covers generating embeddings both inside and outside the Oracle database and effectively storing them within the database for efficient retrieval and processing.
|
| Topic 2 | - Performing Similarity Search: This section tests the skills of Machine Learning Engineers in conducting similarity searches to find relevant data points. It includes performing exact and approximate similarity searches using vector indexes. Candidates will also work with multi-vector similarity search to handle searches across multiple documents for improved retrieval accuracy.
|
| Topic 3 | - Using Vector Indexes: This section evaluates the expertise of AI Database Specialists in optimizing vector searches using indexing techniques. It covers the creation of vector indexes to enhance search speed, including the use of HNSW and IVF vector indexes for performing efficient search queries in AI-driven applications.
|
| Topic 4 | - Understand Vector Fundamentals: This section of the exam measures the skills of Data Engineers in working with vector data types for storing embeddings and enabling semantic queries. It covers vector distance functions and metrics used in AI vector search. Candidates must demonstrate proficiency in performing DML and DDL operations on vectors to manage data efficiently.
|
| Topic 5 | - Building a RAG Application: This section assesses the knowledge of AI Solutions Architects in implementing retrieval-augmented generation (RAG) applications. Candidates will learn to build RAG applications using PL
- SQL and Python to integrate AI models with retrieval techniques for enhanced AI-driven decision-making.
|
Reference: https://education.oracle.com/ouexam-pexam_1z0-184-25/pexam_1Z0-184-25
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