Oracle : 1Z0-184-25 Questions & Answers
Last Updated: Oct 09, 2026
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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 |
| Related Certifications: | Oracle Database 23ai Certified Professional |
| Exam Format: | Multiple Choice |
| Passing Score: | 68% |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 50 |
| Exam Price: | $245 USD |
| 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 HNSW Vector Index for search queries - Use IVF Vector Index for search queries - Create Vector Indexes to speed up AI vector search |
| Using Vector Embeddings | 15% | - Generate Vector Embeddings using ONNX models in the database - Store and Manage Vector Embeddings - Generate Vector Embeddings using External APIs |
| Leveraging Related AI Capabilities | 20% | - Use Oracle GoldenGate for AI data replication - Understand Exadata AI Storage and Distributed AI Processing - Query data using natural language prompts with Select AI - Load and manage vector data using SQL Loader and Oracle Data Pump |
| Performing Similarity Search | 15% | - Perform approximate similarity search using Vector Indexes - Perform Exact Similarity Search - Perform Multi-Vector similarity search for multi-document search |
| Understand Vector Fundamentals | 20% | - Perform DDL Operations on Vectors - Use Vector Data type for storing embeddings and enabling semantic queries - Perform DML Operations on Vectors - Use Vector Distance Functions and Metrics for AI vector search |
| Building a RAG Application | 15% | - Implement RAG using PL/SQL - Understand RAG Architecture and Concepts - Implement RAG using Python |
Common Questions About Oracle AI Vector Search Professional
Candidates should have basic familiarity with Python and AI/ML concepts.
The Oracle AI Vector Search Professional bank is maintained by a team of IT specialists who research the 1Z0-184-25 questions in depth and organize the verified answers into an effective study sequence. Their ongoing work is what keeps the material professional rather than generic.
- Understand Vector Fundamentals (20%)
- Using Vector Embeddings (15%)
- Using Vector Indexes (15%)
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Oracle AI Vector Search Professional Sample Questions:
What is the purpose of the VECTOR_DISTANCE function in Oracle Database 23ai similarity search?
- A. To group vectors by their exact scores
- B. To fetch rows that match exact vector embeddings
- C. To create vector indexes for efficient searches
- D. To calculate the distance between vectors using a specified metric
Correct Answer: D 🗳️
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What is the default distance metric used by the VECTOR_DISTANCE function if none is specified?
- A. Manhattan
- B. Cosine
- C. Euclidean
- D. Hamming
Correct Answer: B 🗳️
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You are tasked with finding the closest matching sentences across books, where each book has multiple paragraphs and sentences. Which SQL structure should you use?
- A. GROUP BY with vector operations
- B. Exact similarity search with a single query vector
- C. A nested query with ORDER BY
- D. FETCH PARTITIONS BY clause
Correct Answer: C 🗳️
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Why would you choose to NOT define a specific size for the VECTOR column during development?
- A. Different external embedding models produce vectors with varying dimensions and data types
- B. It impacts the accuracy of similarity searches
- C. It limits the length of text that can be vectorized
- D. It restricts the database to a single embedding model
Correct Answer: A 🗳️
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In the following Python code, what is the significance of prepending the source filename to each text chunk before storing it in the vector database?
bash
CollapseWrapCopy
docs = [{"text": filename + "|" + section, "path": filename} for filename, sections in faqs.items() for section in sections]
# Sample the resulting data
docs[:2]
- A. It preserves context and aids in the retrieval process by associating each vectorized chunk with its original source file
- B. It improves the accuracy of the LLM by providing additional training data
- C. It helps differentiate between chunks from different files but has no impact on vectorization
- D. It speeds up the vectorization process by providing a unique identifier for each chunk
Correct Answer: A 🗳️
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