Try and practice the latest Oracle : 1Z0-184-25 real questions & answers
Last Updated: Aug 28, 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 Cloud Infrastructure Data Science Oracle Cloud Infrastructure AI Foundations Oracle Database SQL Specialist |
| Available Languages: | English |
| Exam Format: | Multiple Response, Multiple Choice |
| Exam Duration: | 120 minutes |
| Recommended Training: | Oracle Cloud Infrastructure AI Learning Path Oracle University AI Vector Search Training |
| Exam Registration: | Oracle Certification Registration |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored exam via Oracle certification platform |
| Pre Condition: | Recommended: Basic understanding of Oracle Cloud Infrastructure and database concepts; familiarity with AI/ML and embeddings is strongly suggested. |
| Official Syllabus URL: | https://education.oracle.com |
Oracle 1Z0-184-25 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Vector Search Implementation | - Query processing
|
| Oracle AI Vector Search in OCI | - Vector data storage
|
| Foundations of Vector Search | - Similarity metrics
|
| Security, Performance, and Optimization | - Performance tuning
|
| RAG and Generative AI Integration | - LLM integration
|
Everything Candidates Want to Know About the 1Z0-184-25 Exam
The Oracle AI Vector Search Professional exam is the official Oracle assessment behind the Oracle AI Vector Search Professional credential, which sits at the Professional level. Passing it confirms that your skills meet the vendor's current requirements rather than a textbook outline. It also connects with related certifications such as Oracle Cloud Infrastructure AI Foundations, Oracle Cloud Infrastructure Data Science, Oracle Database SQL Specialist, so it can anchor a broader certification path.
The official prerequisites for the Oracle AI Vector Search Professional exam are as follows: Recommended: Basic understanding of Oracle Cloud Infrastructure and database concepts; familiarity with AI/ML and embeddings is strongly suggested.. Requirements can change over time, so confirm the details on the official Oracle exam page at https://education.oracle.com before you book your seat.
You can register through the official channels listed below:
Exam delivery: Online proctored exam via Oracle certification platform.
Oracle points candidates toward the following official training options:
Official training builds the theory; the 62 practice questions from Actual4Cert show you how that theory appears in exam-style items, which is where most study plans actually pay off.
Yes. A free PDF demo of the Oracle AI Vector Search Professional practice questions is available on the Actual4Cert samples page, so you can check the question style and difficulty before spending anything. Every purchase also includes 365 days of free updates, and if your product expires after that period, you can extend the update service from your member zone at 50% off.
If you take the 1Z0-184-25 exam within 60 days of your purchase and do not pass, Actual4Cert offers a 100% money-back guarantee: send a scanned copy of your exam enrollment slip together with the official Score Report PDF within two days of your exam date, and the refund is processed within seven days. The candidate name must match the payer name, and the guarantee does not apply to exams taken within three days of purchase, to material that was downloaded without the exam actually being taken, or to free materials and expired orders. Prefer to keep studying instead? You can exchange your purchase for two additional exam products of equal value, free of charge, while keeping the update service on your original product. Delivery itself is instant: the download link is emailed within one minute of payment, and if nothing arrives within two hours, our support team will sort it out. There is no limit on how many computers you install the material on.
The Oracle AI Vector Search Professional syllabus is organized into 5 domains. The leading areas include Oracle AI Vector Search in OCI, Vector Search Implementation, and RAG and Generative AI Integration. For the complete, topic-by-topic breakdown, scroll up to the Exam Topics section above.
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. Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt
B. Trains a separate LLM on the database and uses it to answer, ignoring the general LLM
C. Clusters similar text chunks and randomly selects one from the most relevant cluster
D. Converts the question to keywords, searches for matches, and inserts the text into the response
Question 2
Which DDL operation is NOT permitted on a table containing a VECTOR column in Oracle Database 23ai?
A. Creating a new table using CTAS (CREATE TABLE AS SELECT) that includes the VECTOR column from the original table
B. Modifying the data type of an existing VECTOR column to a non-VECTOR type
C. Adding a new VECTOR column to the table
D. Dropping an existing VECTOR column from the table
Question 3
What is the advantage of using Euclidean Squared Distance rather than Euclidean Distance in similarity search queries?
A. It is the default distance metric for Oracle AI Vector Search
B. It is simpler and faster because it avoids square-root calculations
C. It guarantees higher accuracy than Euclidean Distance
D. It supports hierarchical partitioning of vectors
Question 4
An application needs to fetch the top-3 matching sentences from a dataset of books while ensuring a balance between speed and accuracy. Which query structure should you use?
A. A combination of relational filters and similarity search
B. Multivector similarity search with approximate fetching and target accuracy
C. Exact similarity search with Euclidean distance
D. Approximate similarity search with the VECTOR_DISTANCE function
Question 5
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 same vector embedding model must be used for vectorizing the data and creating a query vector
B. The storage format of the new dataset (e.g., CSV, JSON)
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
Solutions:
| Question 1 Answer: A | Question 2 Answer: B | Question 3 Answer: B | Question 4 Answer: D | Question 5 Answer: A |
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