100% PASS THE BEST 1Z0-184-25 - FREE ORACLE AI VECTOR SEARCH PROFESSIONAL DOWNLOAD

100% Pass The Best 1Z0-184-25 - Free Oracle AI Vector Search Professional Download

100% Pass The Best 1Z0-184-25 - Free Oracle AI Vector Search Professional Download

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Tags: Free 1Z0-184-25 Download, New 1Z0-184-25 Test Notes, 1Z0-184-25 Authorized Test Dumps, 1Z0-184-25 Valid Braindumps Book, Reliable 1Z0-184-25 Exam Tips

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Oracle 1Z0-184-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • Understand Vector Fundamentals: This section of the exam measures the skills of Data Engineers in working with vector data types for storing embeddings and enabling semantic queries. It covers vector distance functions and metrics used in AI vector search. Candidates must demonstrate proficiency in performing DML and DDL operations on vectors to manage data efficiently.
Topic 3
  • Leveraging Related AI Capabilities: This section evaluates the skills of Cloud AI Engineers in utilizing Oracle’s AI-enhanced capabilities. It covers the use of Exadata AI Storage for faster vector search, Select AI with Autonomous for querying data using natural language, and data loading techniques using SQL Loader and Oracle Data Pump to streamline AI-driven workflows.

Oracle AI Vector Search Professional Sample Questions (Q49-Q54):

NEW QUESTION # 49
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. Use UNION ALL with vector operations
  • C. Perform the similarity search without a WHERE clause
  • D. Apply relational filters and a similarity search in the query

Answer: D

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 # 50
You are tasked with creating a table to store vector embeddings with the following characteristics: Each vector must have exactly 512 dimensions, and the dimensions should be stored as 32-bitfloating point numbers. Which SQL statement should you use?

  • A. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512))
  • B. CREATE TABLE vectors (id NUMBER, embedding VECTOR)
  • C. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32))
  • D. CREATE TABLE vectors (id NUMBER, embedding VECTOR(*, INT8))

Answer: C

Explanation:
In Oracle 23ai, the VECTOR data type can specify dimensions and precision. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32)) (D) defines a column with exactly 512 dimensions and FLOAT32 (32-bit float) format, meeting both requirements. Option A omits the format (defaults vary), risking mismatch. Option B is unspecified, allowing variable dimensions-not "exactly 512." Option C uses INT8, not FLOAT32, and '*' denotes undefined dimensions. Oracle's SQL reference confirms this syntax for precise VECTOR definitions.


NEW QUESTION # 51
Which function should you use to determine the storage format of a vector?

  • A. VECTOR_DIMENSION_FORMAT
  • B. VECTOR_EMBEDDING
  • C. VECTOR_NORM
  • D. VECTOR_CHUNKS

Answer: A


NEW QUESTION # 52
Which vector index available in Oracle Database 23ai is known for its speed and accuracy, making it a preferred choice for vector search?

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

Answer: B

Explanation:
Oracle 23ai supports two main vector indexes: IVF and HNSW. HNSW (D) is renowned for its speed and accuracy, using a hierarchical graph to connect vectors, enabling fast ANN searches with high recall-ideal for latency-sensitive applications like real-time RAG. IVF (C) partitions vectors for scalability but often requires tuning (e.g., NEIGHBOR_PARTITIONS) to match HNSW's accuracy, trading off recall for memory efficiency. BT (A) isn't a 23ai vector index; it's a generic term unrelated here. IFS (B) seems a typo for IVF; no such index exists. HNSW's graph structure outperforms IVF in small-to-medium datasets or where precision matters, as Oracle's documentation and benchmarks highlight, making it a go-to for balanced performance.


NEW QUESTION # 53
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

Answer: C

Explanation:
Oracle Data Pump in 23ai natively supports the VECTOR data type (C), allowing export and import of tables with vector columns without conversion or plug-ins. This facilitates vector search application migrations, preserving dimensional and format integrity (e.g., FLOAT32). BLOB storage (A) isn't required; VECTOR is a distinct type. Data Pump doesn't treat vectors as text (B), avoiding corruption; it handles them as structured arrays. No specialized plug-in (D) is needed; native support is built-in. Oracle's Data Pump documentation confirms seamless handling of VECTOR data.


NEW QUESTION # 54
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