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

Oracle 1Z0-184-25 Online Questions & Answers

• Question 1:

  What security enhancement is introduced in Exadata System Software 24ai?

  A. Integration with third-party security tools
  B. Enhanced encryption algorithm for data at rest
  C. SNMP security (Security Network Management Protocol)
  B. Enhanced encryption algorithm for data at rest

• Question 2:

  How does an application use vector similarity search to retrieve relevant information from a database, and how is this information then integrated into the generation process?

  A. Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt
  B. Trains a separate LLM on the database and uses it to answer, ignoring the general LLM
  C. Converts the question to keywords, searches for matches, and inserts the text into the response
  D. Clusters similar text chunks and randomly selects one from the most relevant cluster
  A. Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt

• Question 3:

  Which is NOT a feature or capability related to AI and Vector Search in Exadata?

  A. Native Support for Vector Search Only within the Database Server
  B. Vector Replication with GoldenGate
  C. Loading Vector Data using SQL*Loader
  D. AI Smart Scan
  A. Native Support for Vector Search Only within the Database Server

• Question 4:

  What is the primary function of AI Smart Scan in Exadata System Software 24ai?

  A. To provide real-time monitoring and diagnostics for AI applications
  B. To accelerate AI workloads by leveraging Exadata RDMA Memory (XRMEM), Exadata Smart Cache, and on-storage processing
  C. To automatically optimize database queries for improved performance
  B. To accelerate AI workloads by leveraging Exadata RDMA Memory (XRMEM), Exadata Smart Cache, and on-storage processing

• Question 5:

  What are the key advantages and considerations of using Retrieval Augmented Generation (RAG) in the context of Oracle AI Vector Search?

  A. It excels at optimizing the performance and efficiency of LLM inference through advanced caching and precomputation techniques, leading to faster response times but potentially increasing storage requirements
  B. It prioritizes real-time data extraction and summarization from various sources to ensure the LLM always has the most up-to-date information
  C. It focuses on training specialized LLMs within the database environment for specific tasks, offering greater control over model behavior and data privacy but potentially requiring more development effort
  D. It leverages existing database security and access controls, thereby enabling secure and controlled access to both the database content and the LLM
  D. It leverages existing database security and access controls, thereby enabling secure and controlled access to both the database content and the LLM

• Question 6:

  You are storing 1,000 embeddings in a VECTOR column, each with 256 dimensions using FLOAT32. What is the approximate size of the data on disk?

  A. 1 MB
  B. 4 MB
  C. 256 KB
  D. 1 GB
  B. 4 MB

• Question 7:

  A machine learning team is using IVF indexes in Oracle Database 23ai to find similar images in a large dataset. During testing, they observe that the search results are often incomplete, missing relevant images. They suspect the issue lies in the number of partitions probed. How should they improve the search accuracy?

  A. Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy
  B. Change the index type to HNSW for better accuracy
  C. Increase the VECTOR_MEMORY_SIZE initialization parameter
  D. Re-create the index with a higher EFCONSTRUCTION value
  A. Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy

• Question 8:

  What is a key characteristic of HNSW vector indexes?

  A. They are hierarchical with multilayered connections
  B. They require exact match for searches
  C. They are disk-based structures
  D. They use hash-based clustering
  A. They are hierarchical with multilayered connections

• Question 9:

  What is the primary function of an embedding model in the context of vector search?

  A. To define the schema for a vector database
  B. To execute similarity search operations within a database
  C. To transform text or data into numerical vector representations
  D. To store vectors in a structured format for efficient retrieval
  C. To transform text or data into numerical vector representations

• Question 10:

  What is the primary purpose of a similarity search in Oracle Database 23ai?

  A. Optimize relational database operations to compute distances between all data points in a database
  B. To find exact matches in BLOB data
  C. To retrieve the most semantically similar entries using distance metrics between different vectors
  D. To group vectors by their exact scores
  C. To retrieve the most semantically similar entries using distance metrics between different vectors