Oracle 1Z0-184-25 Exam Questions - Navigate Your Path to Success

The Oracle Database AI Vector Search Professional (1Z0-184-25) exam is a good choice for Oracle Data Engineers and AI Database Specialists and if the candidate manages to pass Oracle Database AI Vector Search Professional exam, he/she will earn Oracle Database Certification. Below are some essential facts for Oracle 1Z0-184-25 exam candidates:

In actual Oracle Database AI Vector Search Professional (1Z0-184-25) exam, a candidate can expect 50 Questions and the officially allowed time is expected to be around 90 Minutes.
TrendyCerts offers 60 Questions that are based on actual Oracle 1Z0-184-25 syllabus.
Our Oracle 1Z0-184-25 Exam Practice Questions were last updated on: Apr 13, 2025

Sample Questions for Oracle 1Z0-184-25 Exam Preparation

Question 1

What is the primary purpose of a similarity search in Oracle Database 23ai?

AOptimize relational database operations to compute distances between all data points in a database

BTo find exact matches in BLOB data

CTo retrieve the most semantically similar entries using distance metrics between different vectors

DTo group vectors by their exact scores

Correct : C

Similarity search in Oracle 23ai (C) uses vector embeddings in VECTOR columns to retrieve entries semantically similar to a query vector, based on distance metrics (e.g., cosine, Euclidean) via functions like VECTOR_DISTANCE. This is key for AI applications like RAG, finding ''close'' rather than exact matches. Optimizing relational operations (A) is unrelated; similarity search is vector-specific. Exact matches in BLOBs (B) don't leverage vector semantics. Grouping by scores (D) is a post-processing step, not the primary purpose. Oracle's documentation defines similarity search as retrieving semantically proximate vectors.

Options Selected by Other Users:

Question 2

What is the advantage of using Euclidean Squared Distance rather than Euclidean Distance in similarity search queries?

AIt is the default distance metric for Oracle AI Vector Search

BIt supports hierarchical partitioning of vectors

CIt is simpler and faster because it avoids square-root calculations

DIt guarantees higher accuracy than Euclidean Distance

Correct : C

Euclidean Squared Distance (L2-squared) skips the square-root step of Euclidean Distance (L2), i.e., (xi - yi) vs. (xi - yi). Since the square root is monotonic, ranking order remains identical, but avoiding it (C) reduces computational cost, making queries faster---crucial for large-scale vector search. It's not the default metric (A); cosine is often default in Oracle 23ai. It doesn't relate to partitioning (B), an indexing feature. Accuracy (D) is equivalent, as rankings are preserved. Oracle's documentation notes L2-squared as an optimization for performance.