VALID EXAM ORACLE 1Z0-184-25 VCE FREE & VCE 1Z0-184-25 FILE

Valid Exam Oracle 1Z0-184-25 Vce Free & Vce 1Z0-184-25 File

Valid Exam Oracle 1Z0-184-25 Vce Free & Vce 1Z0-184-25 File

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Oracle AI Vector Search Professional Sample Questions (Q26-Q31):

NEW QUESTION # 26
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 guarantees higher accuracy than Euclidean Distance
  • C. It is simpler and faster because it avoids square-root calculations
  • D. It supports hierarchical partitioning of vectors

Answer: C

Explanation:
Euclidean Squared Distance (L2-squared) skips the square-root step of Euclidean Distance (L2), i.e., ∑(xi - yi)² vs. √∑(xi - yi)². 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.


NEW QUESTION # 27
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 # 28
Which statement best describes the capability of Oracle Data Pump for handling vector data in thecontext of vector search applications?

  • A. Data Pump provides native support for exporting and importing tables containing vector data types, facilitating the transfer of vector data for vector search applications
  • B. Because of the complexity of vector data, Data Pump requires a specialized plug-in to handle the export and import operations involving vector data types
  • C. Data Pump treats vector embeddings as regular text strings, which can lead to data corruption or loss of precision when transferring vector data for vector search
  • D. Data Pump only exports and imports vector data if the vector embeddings are stored as BLOB (Binary Large Object) data types in the database

Answer: A

Explanation:
Oracle Data Pump in 23ai natively supports the VECTOR data type (C), allowing export and import of tables with vector columns without conversion or plug-ins. This facilitates vector search application migrations, preserving dimensional and format integrity (e.g., FLOAT32). BLOB storage (A) isn't required; VECTOR is a distinct type. Data Pump doesn't treat vectors as text (B), avoiding corruption; it handles them as structured arrays. No specialized plug-in (D) is needed; native support is built-in. Oracle's Data Pump documentation confirms seamless handling of VECTOR data.


NEW QUESTION # 29
In Oracle Database 23ai, which SQL function calculates the distance between two vectors using the Euclidean metric?

  • A. L1_DISTANCE
  • B. HAMMING_DISTANCE
  • C. L2_DISTANCE
  • D. COSINE_DISTANCE

Answer: C

Explanation:
In Oracle Database 23ai, vector distance calculations are primarily handled by the VECTOR_DISTANCE function, which supports multiple metrics (e.g., COSINE, EUCLIDEAN) specified as parameters (e.g., VECTOR_DISTANCE(v1, v2, EUCLIDEAN)). However, the question implies distinct functions, a common convention in some databases or libraries, and Oracle's documentation aligns L2_DISTANCE (B) with the Euclidean metric. L2 (Euclidean) distance is the straight-line distance between two points in vector space, computed as √∑(xi - yi)², where xi and yi are vector components. For example, for vectors [1, 2] and [4, 6], L2 distance is √((1-4)² + (2-6)²) = √(9 + 16) = 5.
Option A, L1_DISTANCE, represents Manhattan distance (∑|xi - yi|), summing absolute differences-not Euclidean. Option C, HAMMING_DISTANCE, counts differing bits, suited for binary vectors (e.g., INT8), not continuous Euclidean spaces typically used with FLOAT32 embeddings. Option D, COSINE_DISTANCE (1 - cosine similarity), measures angular separation, distinct from Euclidean's magnitude-inclusive approach. While VECTOR_DISTANCE is the general function in 23ai, L2_DISTANCE may be an alias or a contextual shorthand in some Oracle AI examples, reflecting Euclidean's prominence in geometric similarity tasks. Misinterpreting this could lead to choosing COSINE for spatial tasks where magnitude matters, skewing results. Oracle's vector search framework supports Euclidean via VECTOR_DISTANCE, but B aligns with the question's phrasing.


NEW QUESTION # 30
Which vector index available in Oracle Database 23ai is known for its speed and accuracy, making it a preferred choice for vector search?

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

Answer: A

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 # 31
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