PDF Version DemoPhone, tablet, laptop, desktop: the ActualPDF online version runs the Oracle AI Vector Search Professional simulation on any operating system, so 1Z0-184-25 practice happens wherever you happen to be.
Oracle 1Z0-184-25 Exam Overview:
| Certification Vendor: | Oracle |
|---|---|
| Exam Name: | Oracle AI Vector Search Professional |
| Exam Number: | 1Z0-184-25 |
| Exam Price: | USD $245 |
| Related Certifications: | Oracle Database Certification |
| Available Languages: | English |
| Passing Score: | 68% |
| Exam Format: | Multiple Choice |
| Certificate Validity Period: | Cloud Recertification Policy (Retires May 29, 2026) |
| Real Exam Qty: | 50 |
| Exam Duration: | 90 minutes |
| Recommended Training: | Become an Oracle AI Vector Search Professional |
| Exam Registration: | Oracle University Exam Registration |
| Sample Questions: |  |
| Exam Way: | Online proctored or onsite testing center |
| Pre Condition: | Basic familiarity with Python, AI/ML concepts, and Oracle database knowledge |
| Official Syllabus URL: | https://education.oracle.com/products/pexam_1Z0-184-25 |
Oracle 1Z0-184-25 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Performing Similarity Search | 15% | - Multi-vector and multi-document search - Exact and approximate similarity search - Combine vector search with SQL queries |
| Using Vector Indexes | 15% | - Performance vs accuracy trade-offs - Create, manage and optimize vector indexes - Index types: HNSW, IVF |
| Using Vector Embeddings | 15% | - Store and manage embeddings - Generate embeddings inside/outside database - Embedding generation workflows |
| Building a RAG Application | 25% | - Implement with PL/SQL and Python - Integrate with AI services - RAG architecture and components |
| Leveraging Related AI Capabilities | 10% | - Load and manage vector data - Exadata AI Storage and GoldenGate integration - Select AI for natural language queries |
| Understand Vector Fundamentals | 20% | - VECTOR data type usage and storage - Vector distance functions and metrics - Vector concepts and differences from traditional search |
1Z0-184-25 Exam FAQ: Before You Book Your Seat
Oracle AI Vector Search Professional is an official Oracle exam, listed under exam code 1Z0-184-25. A passing result earns you the Oracle AI Vector Search Certified Professional certification at the Professional level. It also ties into Oracle Database Certification, extending its value across your certification roadmap. Employers read this credential as verified competence, which is why it keeps appearing in job requirements.
Expect 50 questions inside 90 minutes on the Oracle AI Vector Search Professional exam. That pace punishes hesitation, so rehearse it: the ActualPDF software engine simulates the real exam scene, reminds you of the questions you got wrong, and pushes you to re-practice them until the clock stops being your enemy.
Passing Oracle AI Vector Search Professional requires 68%, and the official registration fee is USD $245. Retakes charge the full USD $245 again, which is why experienced candidates treat preparation as the cheaper exam fee. Verify your readiness with repeated ActualPDF practice scores above the requirement before you commit to a date.
Basic familiarity with Python, AI/ML concepts, and Oracle database knowledge
Requirements evolve, so confirm the current conditions before registering on the official exam page.
Registration for Oracle AI Vector Search Professional goes through the official channels listed here.
When you schedule, note that the exam is delivered Online proctored or onsite testing center.
Oracle recommends the following training for Oracle AI Vector Search Professional candidates.
Follow any course with the 62 practice questions in the ActualPDF 1Z0-184-25 package; the software engine will even remind you which mistakes need another round.
Yes. ActualPDF provides a free download demo of the Oracle AI Vector Search Professional material, so you can check the content before choosing a version. After purchase, a one-year warranty covers you: the latest version is sent to you as it releases, free for 365 days, and after expiry you can extend the update service at a 50% discount.
Your purchase is covered by a 100% money-back guarantee with clear conditions. Take the Oracle AI Vector Search Professional exam within 60 days of purchase; if you fail, provide your unqualified result by submitting a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and the full refund is processed within 7 days. The exam must match your product, candidate and payer names must match, and attempts within 3 days of purchase, unused downloads, free materials, and expired orders are not covered. Alternatively, exchange for two other exam products of equal value, free, or wait for updates while keeping your original product's update service.
Delivery is instant: files unlock for download at payment and are emailed within one minute. If nothing arrives within 2 hours, check spam and contact customer service, which works 7/24 and normally replies within two hours. Installation is unlimited across your computers.
The Oracle AI Vector Search Professional syllabus spans 6 domains, led by Using Vector Indexes (15%), Using Vector Embeddings (15%), and Building a RAG Application (25%). The complete topic list is published above; candidates who study the map first rarely get lost later.
Oracle AI Vector Search Professional Sample Questions:
Question 1
How does an application use vector similarity search to retrieve relevant information from a database, and how is this information then integrated into the generation process?
A. Clusters similar text chunks and randomly selects one from the most relevant cluster
B. Trains a separate LLM on the database and uses it to answer, ignoring the general LLM
C. Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt
D. Converts the question to keywords, searches for matches, and inserts the text into the response
Question 2
In Oracle Database 23ai, which SQL function calculates the distance between two vectors using the Euclidean metric?
A. COSINE_DISTANCE
B. L2_DISTANCE
C. L1_DISTANCE
D. HAMMING_DISTANCE
Question 3
When generating vector embeddings for a new dataset outside of Oracle Database 23ai, which factor is crucial to ensure meaningful similarity search results?
A. The storage format of the new dataset (e.g., CSV, JSON)
B. The same vector embedding model must be used for vectorizing the data and creating a query vector
C. The choice of programming language used to process the dataset (e.g., Python, Java)
D. The physical location where the vector embeddings are stored
Question 4
When using SQL*Loader to load vector data for search applications, what is a critical consideration regarding the formatting of the vector data within the input CSV file?
A. Use sparse format for vector data
B. Rely on SQL*Loader's automatic normalization of vector data
C. As FVEC is a binary format and the vector dimensions have a known width, fixed offsets can be used to make parsing the vectors fast and efficient
D. Enclose vector components in curly braces ({})
Question 5
What is the purpose of the Vector Pool in Oracle Database 23ai?
A. To manage database partitioning
B. To store non-vector data types
C. To store HNSW vector indexes and IVF index metadata
D. To enable longer SQL execution
Solutions:
| Question 1 Answer: C | Question 2 Answer: B | Question 3 Answer: B | Question 4 Answer: D | Question 5 Answer: C |
