ORACLE OFFERS ORACLE 1Z0-184-25 DUMPS WITH REFUND GUARANTY

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Oracle 1Z0-184-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Performing Similarity Search: This section tests the skills of Machine Learning Engineers in conducting similarity searches to find relevant data points. It includes performing exact and approximate similarity searches using vector indexes. Candidates will also work with multi-vector similarity search to handle searches across multiple documents for improved retrieval accuracy.
Topic 2
  • Building a RAG Application: This section assesses the knowledge of AI Solutions Architects in implementing retrieval-augmented generation (RAG) applications. Candidates will learn to build RAG applications using PL
  • SQL and Python to integrate AI models with retrieval techniques for enhanced AI-driven decision-making.
Topic 3
  • Using Vector Indexes: This section evaluates the expertise of AI Database Specialists in optimizing vector searches using indexing techniques. It covers the creation of vector indexes to enhance search speed, including the use of HNSW and IVF vector indexes for performing efficient search queries in AI-driven applications.

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Oracle AI Vector Search Professional Sample Questions (Q56-Q61):

NEW QUESTION # 56
Which is a characteristic of an approximate similarity search in Oracle Database 23ai?

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

Answer: C

Explanation:
Approximate similarity search (ANN) in Oracle 23ai (B) uses indexes (e.g., HNSW, IVF) to trade accuracy for speed, returning near-matches faster by not comparing all vectors. Exact search compares every vector (A), not ANN. It doesn't guarantee 100% accuracy (C); that's exact search. It's faster, not slower (D), than exact search due to indexing. Oracle's documentation defines ANN's speed-accuracy trade-off as its hallmark.


NEW QUESTION # 57
Which PL/SQL function converts documents such as PDF, DOC, JSON, XML, or HTML to plain text?

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

Answer: C

Explanation:
In Oracle Database 23ai, DBMS_VECTOR_CHAIN.UTL_TO_TEXT is the PL/SQL function that converts documents in formats like PDF, DOC, JSON, XML, or HTML into plain text, a key step in preparing data for vectorization in RAG workflows. DBMS_VECTOR.TEXT_TO_PLAIN (A) is not a valid function. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS (C) splits text into smaller segments, not converts documents. DBMS_VECTOR.CONVERT_TO_TEXT (D) does not exist in the documented packages. UTL_TO_TEXT is part of the DBMS_VECTOR_CHAIN package, designed for vector processing pipelines, and is explicitly noted for document conversion in Oracle's documentation.


NEW QUESTION # 58
When generating vector embeddings outside the database, what is the most suitable option for storing the embeddings for later use?

  • A. In a dedicated vector database
  • B. In a CSV file
  • C. In the database as BLOB (Binary Large Object) data
  • D. In a binary FVEC file with the relational data in a CSV file

Answer: A

Explanation:
When vector embeddings are generated outside the database, the storage choice must balance efficiency, scalability, and usability for similarity search. A CSV file (A) is simple and human-readable but inefficient for large-scale vector operations due to text parsing overhead and lack of indexing support. A binary FVEC file (B) offers a compact format for vectors, reducing storage size and improving read performance, but separating relational data into a CSV complicates integration and querying, making it suboptimal for unified workflows. Storing embeddings as BLOBs in a relational database (C) integrates well with structured data and supports SQL access, but it lacks the specialized indexing (e.g., HNSW, IVF) and query optimizations that dedicated vector databases provide. A dedicated vector database (D), such as Milvus or Pinecone (or Oracle 23ai's vector capabilities if internal), is purpose-built for high-dimensional vectors, offering efficient storage, advanced indexing, and fast approximate nearest neighbor (ANN) searches. For external generation scenarios, where embeddings are not immediately integrated into Oracle 23ai, a dedicated vector database is the most suitable due to its performance and scalability advantages. Oracle's AI Vector Search documentation indirectly supports this by emphasizing optimized vector storage for search efficiency, though it focuses on in-database solutions.


NEW QUESTION # 59
You are asked to fetch the top five vectors nearest to a query vector, but only for a specific category of documents. Which query structure should you use?

  • A. Use VECTOR_INDEX_HINT and NO WHERE clause
  • B. Perform the similarity search without a WHERE clause
  • C. Apply relational filters and a similarity search in the query
  • D. Use UNION ALL with vector operations

Answer: C

Explanation:
To fetch the top five nearest vectors for a specific category, combine relational filtering (e.g., WHERE category = 'X') with similarity search (C) (e.g., VECTOR_DISTANCE with ORDER BY and FETCH FIRST 5 ROWS). UNION ALL (A) is for combining result sets, not filtering. Omitting WHERE (B) ignores the category constraint. VECTOR_INDEX_HINT (D) influences index usage, not filtering, and skipping WHERE misses the requirement. Oracle's vector search examples use WHERE clauses with similarity functions for such tasks.


NEW QUESTION # 60
Why would you choose to NOT define a specific size for the VECTOR column during development?

  • A. Different external embedding models produce vectors with varying dimensions and data types
  • B. It restricts the database to a single embedding model
  • C. It limits the length of text that can be vectorized
  • D. It impacts the accuracy of similarity searches

Answer: A

Explanation:
In Oracle Database 23ai, a VECTOR column can be defined with a specific size (e.g., VECTOR(512, FLOAT32)) or left unspecified (e.g., VECTOR). Not defining a size (D) provides flexibility during development because different embedding models (e.g., BERT, SentenceTransformer) generate vectors with varying dimensions (e.g., 768, 384) and data types (e.g., FLOAT32, INT8). This avoids locking the schema into one model, allowing experimentation. Accuracy (A) isn't directly impacted by size definition; it depends on the model and metric. A fixed size doesn't restrict the database to one model (B) but requires matching dimensions. Text length (C) affects tokenization, not vector dimensions. Oracle's documentation supports undefined VECTOR columns for flexibility in AI workflows.


NEW QUESTION # 61
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