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

Oracle 1Z0-184-25 Online Questions & Answers

• Question 21:

  Which of the following actions will result in an error when using VECTOR_DIMENSION_COUNT() in Oracle Database 23ai?

  A. Providing a vector with a dimensionality that exceeds the specified dimension count
  B. Using a vector with a data type that is not supported by the function
  C. Providing a vector with duplicate values for its components
  D. Calling the function on a vector that has been created with TO_VECTOR()
  B. Using a vector with a data type that is not supported by the function

• Question 22:

  What is a key advantage of using GoldenGate 23ai for managing and distributing vector data for AI applications?

  A. Real-time vector data updates across locations
  B. Automatic translation of vector embeddings between formats
  C. Specialized vector embedding compression
  D. Built-in version control for vector data
  A. Real-time vector data updates across locations

• Question 23:

  How is the security interaction between Autonomous Database and OCI Generative AI managed in the context of Select AI?

  A. By encrypting all communication between the Autonomous Database and OCI Generative AI using TLS/SSL protocols
  B. By utilizing Resource Principals, which grant the Autonomous Database instance access to OCI Generative AI without exposing sensitive credentials
  C. By establishing a secure VPN tunnel between the Autonomous Database and OCI Generative AI service
  D. By requiring users to manually enter their OCI API keys each time they execute a natural language query
  B. By utilizing Resource Principals, which grant the Autonomous Database instance access to OCI Generative AI without exposing sensitive credentials

• Question 24:

  What is the first step in setting up the practice environment for Select AI?

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

• Question 25:

  Which Python library is used to vectorize text chunks and the user's question in the following example?

  import oracledb

  connection = oracledb.connect(user=un, password=pw, dsn=ds) table_name = "Page"

  with connection.cursor() as cursor:

  create_table_sql = f"""

  CREATE TABLE IF NOT EXISTS {table_name} (

  id NUMBER PRIMARY KEY,

  payload CLOB CHECK (payload IS JSON),

  vector VECTOR

  )"""

  try:

  cursor.execute(create_table_sql)

  except oracledb.DatabaseError as e:

  raise

  connection.autocommit = True

  from sentence_transformers import SentenceTransformer

  encoder = SentenceTransformer('all-MiniLM-L12-v2')

  A. sentence_transformers
  B. oci
  C. oracledb
  D. json
  A. sentence_transformers

• Question 26:

  Which PL/SQL package is primarily used for interacting with Generative AI services in Oracle Database 23ai?

  A. DBMS_AI
  B. DBMS_ML
  C. DBMS_VECTOR_CHAIN
  D. DBMS_GENAI
  A. DBMS_AI

• Question 27:

  A database administrator wants to change the VECTOR_MEMORY_SIZE parameter for a pluggable database (PDB) in Oracle Database 23ai. Which SQL command is correct?

  A. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH
  B. ALTER DATABASE SET VECTOR_MEMORY_SIZE=1G SCOPE=VECTOR
  C. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=SGA
  D. ALTER SYSTEM RESET VECTOR_MEMORY_SIZE
  A. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH

• Question 28:

  When using SQL*Loader to load vector data for search applications, what is a critical consideration regarding the formatting of the vector data within the input CSV file?

  A. Enclose vector components in curly braces ({})
  B. As FVEC is a binary format and the vector dimensions have a known width, fixed offsets can be used to make parsing the vectors fast and efficient
  C. Use sparse format for vector data
  D. Rely on SQL*Loader's automatic normalization of vector data
  A. Enclose vector components in curly braces ({})

• Question 29:

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

• Question 30:

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