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Hot 1Z0-184-25 Formal Test | Professional Oracle New 1Z0-184-25 Exam Online: Oracle AI Vector Search Professional

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Oracle AI Vector Search Professional Sample Questions (Q19-Q24):

NEW QUESTION # 19
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

A. To calculate vector distances
B. To calculate vector dimensions
C. To generate a single vector embedding for data
D. To serialize vectors into a string

Answer: C

Explanation:
The VECTOR_EMBEDDING function in Oracle 23ai (D) generates a vector embedding from input data (e.g., text) using a specified model (e.g., ONNX), producing a single VECTOR-type output for similarity search or AI tasks. It doesn't calculate dimensions (A); VECTOR_DIMENSION_COUNT does that. It doesn't compute distances (B); VECTOR_DISTANCE is for that. It doesn't serialize vectors (C); VECTOR_SERIALIZE handles serialization. Oracle's documentation positions VECTOR_EMBEDDING as the core function for in-database embedding creation, central to vector search workflows.

NEW QUESTION # 20
What is the primary purpose of a similarity search in Oracle Database 23ai?

A. Optimize relational database operations to compute distances between all data points in a database
B. To group vectors by their exact scores
C. To retrieve the most semantically similar entries using distance metrics between different vectors
D. To find exact matches in BLOB data

Answer: C

Explanation:
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.

NEW QUESTION # 21
Which vector index available in Oracle Database 23ai is known for its speed and accuracy, making it a preferred choice for vector search?

A. Inverted File System (IFS) index
B. Hierarchical Navigable Small World (HNSW) index
C. Inverted File (IVF) index
D. Binary Tree (BT) index

Answer: B

Explanation:
Oracle 23ai supports two main vector indexes: IVF and HNSW. HNSW (D) is renowned for its speed and accuracy, using a hierarchical graph to connect vectors, enabling fast ANN searches with high recall-ideal for latency-sensitive applications like real-time RAG. IVF (C) partitions vectors for scalability but often requires tuning (e.g., NEIGHBOR_PARTITIONS) to match HNSW's accuracy, trading off recall for memory efficiency. BT (A) isn't a 23ai vector index; it's a generic term unrelated here. IFS (B) seems a typo for IVF; no such index exists. HNSW's graph structure outperforms IVF in small-to-medium datasets or where precision matters, as Oracle's documentation and benchmarks highlight, making it a go-to for balanced performance.

NEW QUESTION # 22
You want to quickly retrieve the top-10 matches for a query vector from a dataset of billions of vectors, prioritizing speed over exact accuracy. What is the best approach?

A. Exact similarity search using flat search
B. Relational filtering combined with an exact search
C. Approximate similarity search with a low target accuracy setting
D. Exact similarity search with a high target accuracy setting

Answer: C

Explanation:
For speed over accuracy with billions of vectors, approximate similarity search (ANN) with a low target accuracy setting (B) (e.g., 70%) uses indexes like HNSW or IVF, probing fewer vectors to return top-10 matches quickly. Exact flat search (A) scans all vectors, too slow for billions. Relational filtering with exact search (C) adds overhead without speed gains. Exact search with high accuracy (D) maximizes precision but sacrifices speed. Oracle's documentation recommends ANN for large-scale, speed-focused queries.

NEW QUESTION # 23
What is the first step in setting up the practice environment for Select AI?

A. Create a new user account with elevated privileges
B. Drop any compartment that does not use OCI Generative AI
C. Optionally create an OCI compartment
D. Create a policy to enable access to OCI Generative AI

Answer: C

Explanation:
Select AI in Oracle Database 23ai enables natural language queries by integrating with OCI Generative AI services. The first step in setting up the practice environment is to optionally create an OCI compartment (A), which organizes and isolates resources in Oracle Cloud Infrastructure (OCI). This is foundational because subsequent steps-like defining policies or configuring the Autonomous Database-depend on a compartment structure, though an existing compartment can be reused, making it optional. Creating a policy (B) is a subsequent step to grant access to OCIGenerative AI, requiring a compartment first. Dropping compartments (C) is irrelevant and disruptive. Creating a user account (D) is not specified as the initial step in Select AI setup. Oracle's Select AI documentation lists compartment setup as the starting point in OCI configuration.

NEW QUESTION # 24
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