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

Oracle 1Z0-184-25 Online Questions & Answers

• Question 41:

  You are working with vector search in Oracle Database 23ai and need to ensure the integrity of your vector data during storage and retrieval. Which factor is crucial for maintaining the accuracy and reliability of your vector search results?

  A. Using the same embedding model for both vector creation and similarity search
  B. Regularly updating vector embeddings to reflect changes in the source data
  C. The specific distance algorithm employed for vector comparisons
  D. The physical storage location of the vector data
  A. Using the same embedding model for both vector creation and similarity search

• Question 42:

  An application needs to fetch the top-3 matching sentences from a dataset of books while ensuring a balance between speed and accuracy. Which query structure should you use?

  A. Approximate similarity search with the VECTOR_DISTANCE function
  B. Exact similarity search with Euclidean distance
  C. Multivector similarity search with approximate fetching and target accuracy
  D. A combination of relational filters and similarity search
  A. Approximate similarity search with the VECTOR_DISTANCE function

• Question 43:

  What is the purpose of the Vector Pool in Oracle Database 23ai?

  A. To manage database partitioning
  B. To store HNSW vector indexes and IVF index metadata
  C. To enable longer SQL execution
  D. To store non-vector data types
  B. To store HNSW vector indexes and IVF index metadata

• Question 44:

  If a query vector uses a different distance metric than the one used to create the index, whathappens?

  A. The query fails
  B. An exact match search is triggered
  C. The index automatically updates
  D. A warning is logged, but the query executes
  A. The query fails

• Question 45:

  What is the primary difference between the HNSW and IVF vector indexes in Oracle Database 23ai?

  A. Both operate identically but differ in memory usage
  B. HNSW guarantees accuracy, whereas IVF sacrifices performance for accuracy
  C. HNSW uses an in-memory neighbor graph for faster approximate searches, whereas IVF uses the buffer cache with partitions
  D. HNSW is partition-based, whereas IVF uses neighbor graphs for indexing
  C. HNSW uses an in-memory neighbor graph for faster approximate searches, whereas IVF uses the buffer cache with partitions

• Question 46:

  What happens when querying with an IVF index if you increase the value of the NEIGHBOR_PARTITIONS probes parameter?

  A. The number of centroids decreases
  B. Accuracy decreases
  C. Index creation time is reduced
  D. More partitions are probed, improving accuracy, but also increasing query latency
  D. More partitions are probed, improving accuracy, but also increasing query latency

• Question 47:

  When generating vector embeddings for a new dataset outside of Oracle Database 23ai, which factor is crucial to ensure meaningful similarity search results?

  A. The choice of programming language used to process the dataset (e.g., Python, Java)
  B. The physical location where the vector embeddings are stored
  C. The storage format of the new dataset (e.g., CSV, JSON)
  D. The same vector embedding model must be used for vectorizing the data and creating a query vector
  D. The same vector embedding model must be used for vectorizing the data and creating a query vector

• Question 48:

  Which statement best describes the capability of Oracle Data Pump for handling vector data in thecontext of vector search applications?

  A. Data Pump only exports and imports vector data if the vector embeddings are stored as BLOB (Binary Large Object) data types in the database
  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. Data Pump provides native support for exporting and importing tables containing vector data types, facilitating the transfer of vector data for vector search applications
  D. 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 provides native support for exporting and importing tables containing vector data types, facilitating the transfer of vector data for vector search applications

• Question 49:

  Which is a characteristic of an approximate similarity search in Oracle Database 23ai?

  A. It compares every vector in the dataset
  B. It trades off accuracy for faster performance
  C. It always guarantees 100% accuracy
  D. It is slower than exact similarity search
  B. It trades off accuracy for faster performance

• Question 50:

  What is the purpose of the VECTOR_DISTANCE function in Oracle Database 23ai similarity search?

  A. To fetch rows that match exact vector embeddings
  B. To create vector indexes for efficient searches
  C. To group vectors by their exact scores
  D. To calculate the distance between vectors using a specified metric
  D. To calculate the distance between vectors using a specified metric