Oracle : 1Z0-184-25 Questions & Answers
Last Updated: Oct 06, 2026
No. of Questions: 62 Questions & Answers with Testing Engine
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How should you prepare for the Oracle AI Vector Search Professional exam? The efficient answer: work through the 62+ Q&As covering the 1Z0-184-25 objectives at ActualTorrent, refreshed for 2026, until exam day feels like a formality.
Oracle 1Z0-184-25 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Oracle AI Vector Search in OCI | - Vector data storage
|
| Topic 2: Vector Search Implementation | - Query processing
|
| Topic 3: RAG and Generative AI Integration | - LLM integration
|
| Topic 4: Foundations of Vector Search | - Vector embeddings concepts
|
| Topic 5: Security, Performance, and Optimization | - Performance tuning
|
1Z0-184-25 Exam FAQ: What Candidates Really Want to Know
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Recommended: Basic understanding of Oracle Cloud Infrastructure and database concepts; familiarity with AI/ML and embeddings is strongly suggested. These rules are set by Oracle and can change, so confirm the current requirements on the official exam page: https://education.oracle.com before registering for the Oracle AI Vector Search Professional exam.
The 1Z0-184-25 exam is Oracle's official certification test, and passing it awards the Oracle AI Vector Search Professional credential — a certification at the Professional level. Its career value is simple: certified professionals are the first shortlisted for better roles and promotions, which is why candidate numbers keep climbing. It also connects to Oracle Cloud Infrastructure AI Foundations, Oracle Cloud Infrastructure Data Science, Oracle Database SQL Specialist, extending its value across a wider credential path.
Registration goes through Oracle's official channels:
The exam itself is delivered via Online proctored exam via Oracle certification platform, so factor that into your scheduling.
The Oracle AI Vector Search Professional syllabus spans 5 knowledge areas. The heaviest are Foundations of Vector Search, Oracle AI Vector Search in OCI, RAG and Generative AI Integration. Match your study time to those weights — the full outline is listed in the section above.
Yes — these are the vendor's recommended training options:
Training builds the base; the 62 practice questions for the 1Z0-184-25 exam from ActualTorrent show whether it holds up when the clock is running.
Oracle AI Vector Search Professional Sample Questions:
What is the primary difference between the HNSW and IVF vector indexes in Oracle Database 23ai?
- A. HNSW guarantees accuracy, whereas IVF sacrifices performance for accuracy
- B. HNSW is partition-based, whereas IVF uses neighbor graphs for indexing
- C. Both operate identically but differ in memory usage
- D. HNSW uses an in-memory neighbor graph for faster approximate searches, whereas IVF uses the buffer cache with partitions
Correct Answer: D 🗳️
If a query vector uses a different distance metric than the one used to create the index, whathappens?
- A. The index automatically updates
- B. An exact match search is triggered
- C. The query fails
- D. A warning is logged, but the query executes
Correct Answer: C 🗳️
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A machine learning team is using IVF indexes in Oracle Database 23ai to find similar images in a large dataset. During testing, they observe that the search results are often incomplete, missing relevant images. They suspect the issue lies in the number of partitions probed. How should they improve the search accuracy?
- A. Re-create the index with a higher EFCONSTRUCTION value
- B. Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy
- C. Increase the VECTOR_MEMORY_SIZE initialization parameter
- D. Change the index type to HNSW for better accuracy
Correct Answer: B 🗳️
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In Oracle Database 23ai, which SQL function calculates the distance between two vectors using the Euclidean metric?
- A. L2_DISTANCE
- B. HAMMING_DISTANCE
- C. L1_DISTANCE
- D. COSINE_DISTANCE
Correct Answer: A 🗳️
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What is the correct order of steps for building a RAG application using PL/SQL in Oracle Database 23ai?
- A. Load ONNX Model, Vectorize Question, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
- B. Load Document, Split Text into Chunks, Load ONNX Model, Create Embeddings, Vectorize Question, Perform Vector Search, Generate Output
- C. Load Document, Load ONNX Model, Split Text into Chunks, Create Embeddings, VectorizeQuestion, Perform Vector Search, Generate Output
- D. Vectorize Question, Load ONNX Model, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
Correct Answer: B 🗳️
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