Oracle 1Z0-184-25 Online Practice Questions and Exam Preparation

Oracle 1Z0-184-25 Online Questions & Answers

• Question 31:

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

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

• Question 32:

  Which PL/SQL function converts documents such as PDF, DOC, JSON, XML, or HTML to plain text?

  A. DBMS_VECTOR.TEXT_TO_PLAIN
  B. DBMS_VECTOR_CHAIN.UTL_TO_TEXT
  C. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS
  D. DBMS_VECTOR.CONVERT_TO_TEXT
  B. DBMS_VECTOR_CHAIN.UTL_TO_TEXT

• Question 33:

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

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

• Question 34:

  Which SQL function is used to create a vector embedding for a given text string in Oracle Database 23ai?

  A. GENERATE_EMBEDDING
  B. CREATE_VECTOR_EMBEDDING
  C. EMBED_TEXT
  D. VECTOR_EMBEDDING
  D. VECTOR_EMBEDDING

• Question 35:

  Which function is used to generate vector embeddings within an Oracle database?

  A. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS
  B. DBMS_VECTOR_CHAIN.UTL_TO_TEXT
  C. DBMS_VECTOR_CHAIN.UTL_TO_EMBEDDINGS
  D. DBMS_VECTOR_CHAIN.UTL_TO_GENERATE_TEXT
  C. DBMS_VECTOR_CHAIN.UTL_TO_EMBEDDINGS

• Question 36:

  What is the function of the COSINE parameter in the SQL query used to retrieve similar vectors?

  topk = 3

  sql = f"""select payload, vector_distance(vector, :vector, COSINE) as score

  from {table_name}

  order by score

  fetch approximate {topk} rows only"""

  A. It filters out vectors with a cosine similarity below a certain threshold
  B. It converts the vectors to a format compatible with the SQL database
  C. It indicates that the cosine distance metric should be used to measure similarity between vectors
  D. It specifies the type of vector encoding used in the database
  C. It indicates that the cosine distance metric should be used to measure similarity between vectors

• Question 37:

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

• Question 38:

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

• Question 39:

  You need to generate a vector from the string '[1.2, 3.4]' in FLOAT32 format with 2 dimensions. Which function will you use?

  A. TO_VECTOR
  B. VECTOR_DISTANCE
  C. FROM_VECTOR
  D. VECTOR_SERIALIZE
  A. TO_VECTOR

• Question 40:

  What is the significance of using local ONNX models for embedding within the database?

  A. Support for legacy SQL*Plus clients
  B. Improved accuracy compared to external models
  C. Reduced embedding dimensions for faster processing
  D. Enhanced security because data remains within the database
  D. Enhanced security because data remains within the database