Exam Name: Oracle AI Vector Search Professional
Certification Provider: Oracle
Corresponding Certification: Oracle Database 23ai
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- Total Questions: 62
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Oracle 1Z0-184-25 Exam Overview:
| Certification Vendor: | Oracle |
|---|---|
| Exam Name: | Oracle AI Vector Search Professional |
| Exam Number: | 1Z0-184-25 |
| Available Languages: | English |
| Exam Duration: | 90 minutes |
| Related Certifications: | Oracle Database 23ai Certified Professional |
| Exam Price: | $245 USD |
| Passing Score: | 68% |
| Exam Format: | Multiple Choice |
| Real Exam Qty: | 50 |
| Certificate Validity Period: | 2 years |
| Sample Questions: | DOWNLOAD DEMO |
| 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:
| Section | Weight | Objectives |
|---|---|---|
| Using Vector Indexes | 15% | - Use IVF Vector Index for search queries - Create Vector Indexes to speed up AI vector search - Use HNSW Vector Index for search queries |
| Performing Similarity Search | 15% | - Perform Exact Similarity Search - Perform Multi-Vector similarity search for multi-document search - Perform approximate similarity search using Vector Indexes |
| Leveraging Related AI Capabilities | 20% | - Query data using natural language prompts with Select AI - Understand Exadata AI Storage and Distributed AI Processing - Load and manage vector data using SQL Loader and Oracle Data Pump - Use Oracle GoldenGate for AI data replication |
| Using Vector Embeddings | 15% | - Store and Manage Vector Embeddings - Generate Vector Embeddings using External APIs - Generate Vector Embeddings using ONNX models in the database |
| Building a RAG Application | 15% | - Understand RAG Architecture and Concepts - Implement RAG using Python - Implement RAG using PL/SQL |
| Understand Vector Fundamentals | 20% | - Perform DML Operations on Vectors - Perform DDL Operations on Vectors - Use Vector Distance Functions and Metrics for AI vector search - Use Vector Data type for storing embeddings and enabling semantic queries |
1Z0-184-25 Material FAQ
Memorizing which option is correct works only until the question is reworded. Understanding the concept behind each 1Z0-184-25 question lets you handle variations with confidence. Take notes as you practice, mark the key points, and your study sessions build real, durable knowledge.
The Oracle AI Vector Search Professional practice questions are organized around the official exam objectives. Key topics include:
- Building a RAG Application (15%)
- Using Vector Indexes (15%)
- Understand Vector Fundamentals (20%)
Questions across these topics span a range of difficulty levels, so both newer and experienced candidates find appropriate challenges.
The Oracle AI Vector Search Professional exam contains 50 questions, and you will have 90 minutes minutes to complete them. Practicing within the same limits builds the pacing the real exam demands.
This package includes 62 practice questions for the Oracle AI Vector Search Professional exam. Every answer is verified by an experienced team of experts, so you can trust what you study.
Candidates should have basic familiarity with Python and AI/ML concepts.
You can confirm the current eligibility requirements on the official 1Z0-184-25 exam page before you register.
The Oracle AI Vector Search Professional exam requires a score of 68% to pass, and the official registration fee is $245 USD. Because the fee is significant, preparing thoroughly the first time is the practical choice.
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Oracle AI Vector Search Professional Sample Questions:
You are storing 1,000 embeddings in a VECTOR column, each with 256 dimensions using FLOAT32. What is the approximate size of the data on disk?
- A. 1 MB
- B. 1 GB
- C. 4 MB
- D. 256 KB
Correct Answer: C 🗳️
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What is a key advantage of using GoldenGate 23ai for managing and distributing vector data for AI applications?
- A. Real-time vector data updates across locations
- B. Automatic translation of vector embeddings between formats
- C. Specialized vector embedding compression
- D. Built-in version control for vector data
Correct Answer: A 🗳️
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What is the function of the COSINE parameter in the SQL query used to retrieve similar vectors?
topk = 3
sql = f"""select payload, vector_distance(vector, :vector, COSINE) as score from {table_name} order by score fetch approximate {topk} rows only"""
- A. It specifies the type of vector encoding used in the database
- B. It filters out vectors with a cosine similarity below a certain threshold
- C. It converts the vectors to a format compatible with the SQL database
- D. It indicates that the cosine distance metric should be used to measure similarity between vectors
Correct Answer: D 🗳️
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What is the significance of using local ONNX models for embedding within the database?
- A. Support for legacy SQL*Plus clients
- B. Reduced embedding dimensions for faster processing
- C. Improved accuracy compared to external models
- D. Enhanced security because data remains within the database
Correct Answer: D 🗳️
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In Oracle Database 23ai, which data type is used to store vector embeddings for similarity search?
- A. VECTOR2
- B. BLOB
- C. VECTOR
- D. VARCHAR2
Correct Answer: C 🗳️
Explanation: Only visible for Exams4sures members. You can sign-up / login (it's free).
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