LATEST 1Z0-184-25 EXAM LABS - ACTUAL 1Z0-184-25 TEST PDF

Latest 1Z0-184-25 Exam Labs - Actual 1Z0-184-25 Test Pdf

Latest 1Z0-184-25 Exam Labs - Actual 1Z0-184-25 Test Pdf

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Oracle AI Vector Search Professional Sample Questions (Q48-Q53):

NEW QUESTION # 48
Which SQL statement correctly adds a VECTOR column named "v" with 4 dimensions and FLOAT32 format to an existing table named "my_table"?

  • A. ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32))
  • B. UPDATE my_table SET v = VECTOR(4, FLOAT32)
  • C. ALTER TABLE my_table ADD v VECTOR(4, FLOAT32)
  • D. ALTER TABLE my_table MODIFY (v VECTOR(4, FLOAT32))

Answer: A

Explanation:
To add a new column to an existing table, Oracle uses the ALTER TABLE statement with the ADD clause. Option B, ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32)), correctly specifies the column name "v", the VECTOR type, and its attributes (4 dimensions, FLOAT32 precision) within parentheses, aligning with Oracle's DDL syntax for VECTOR columns. Option A uses MODIFY, which alters existing columns, not adds new ones, making it incorrect here. Option C uses UPDATE, a DML statement for updating data, not a DDL operation for schema changes. Option D omits parentheses around the VECTOR specification, which is syntactically invalid as Oracle requires dimensions and format to be enclosed. The SQL Language Reference confirms this syntax for adding VECTOR columns.


NEW QUESTION # 49
What is the advantage of using Euclidean Squared Distance rather than Euclidean Distance in similarity search queries?

  • A. It supports hierarchical partitioning of vectors
  • B. It is simpler and faster because it avoids square-root calculations
  • C. It guarantees higher accuracy than Euclidean Distance
  • D. It is the default distance metric for Oracle AI Vector Search

Answer: B

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 # 50
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. 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
  • C. 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
  • 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 # 51
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 # 52
You need to prioritize accuracy over speed in a similarity search for a dataset of images. Which should you use?

  • A. Approximate similarity search with HNSW indexing and target accuracy of 70%
  • B. Approximate similarity search with IVF indexing and target accuracy of 70%
  • C. Exact similarity search using a full table scan
  • D. Multivector similarity search with partitioning

Answer: C

Explanation:
To prioritize accuracy over speed, exact similarity search with a full table scan (C) computes distances between the query vector and all stored vectors, guaranteeing 100% recall without approximation trade-offs. HNSW with 70% target accuracy (A) and IVF with 70% (D) are approximate methods, sacrificing accuracy for speed via indexing (e.g., probing fewer neighbors). Multivector search (B) isn't a standard Oracle 23ai term; partitioning aids scale, not accuracy. Exact search, though slower, ensures maximum accuracy, as per Oracle's vector search options.


NEW QUESTION # 53
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