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Free PDF Valid 1Z0-184-25 - Oracle AI Vector Search Professional Reliable Dumps

Oracle PDF Questions format, web-based practice test, and desktop-based 1Z0-184-25 practice test formats. All these three 1Z0-184-25 exam dumps formats features surely will help you in preparation and boost your confidence to pass the challenging Oracle 1Z0-184-25 Exam with good scores.

Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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 3	Using Vector Embeddings: This section measures the abilities of AI Developers in generating and storing vector embeddings for AI applications. It covers generating embeddings both inside and outside the Oracle database and effectively storing them within the database for efficient retrieval and processing.

Oracle AI Vector Search Professional Sample Questions (Q13-Q18):

NEW QUESTION # 13
Which of the following actions will result in an error when using VECTOR_DIMENSION_COUNT() in Oracle Database 23ai?

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

Answer: D

Explanation:
The VECTOR_DIMENSION_COUNT() function in Oracle 23ai returns the number of dimensions in a VECTOR-type value (e.g., 512 for VECTOR(512, FLOAT32)). It's a metadata utility, not a validator of content or structure beyond type compatibility. Option B-using a vector with an unsupported data type-causes an error because the function expects a VECTOR argument; passing, say, a VARCHAR2 or NUMBER instead (e.g., '1,2,3' or 42) triggers an ORA-error (e.g., ORA-00932: inconsistent datatypes). Oracle enforces strict typing for vector functions.
Option A (exceeding specified dimensions) is a red herring; the function reports the actual dimension count of the vector, not the column's defined limit-e.g., VECTOR_DIMENSION_COUNT(TO_VECTOR('[1,2,3]')) returns 3, even if the column is VECTOR(2), as the error occurs at insertion, not here. Option C (duplicate values, like [1,1,2]) is valid; the function counts dimensions (3), ignoring content. Option D (using TO_VECTOR()) is explicitly supported; VECTOR_DIMENSION_COUNT(TO_VECTOR('[1.2, 3.4]')) returns 2 without issue. Misinterpreting this could lead developers to over-constrain data prematurely-B's type mismatch is the clear error case, rooted in Oracle's vector type system.

NEW QUESTION # 14
Which parameter is used to define the number of closest vector candidates considered during HNSW index creation?

A. EFCONSTRUCTION
B. NEIGHBOURS
C. TARGET_ACCURACY
D. VECTOR_MEMORY_SIZE

Answer: A

Explanation:
In Oracle 23ai, EFCONSTRUCTION (A) controls the number of closest vector candidates (edges) considered during HNSW index construction, affecting the graph's connectivity and search quality. Higher values improve accuracy but increase build time. VECTOR_MEMORY_SIZE (B) sets memory allocation, not candidate count. NEIGHBOURS (C) isn't a parameter; it might confuse with NEIGHBOR_PARTITIONS (IVF). TARGET_ACCURACY (D) adjusts query-time accuracy, not index creation. Oracle's HNSW documentation specifies EFCONSTRUCTION for this purpose.

NEW QUESTION # 15
What is the significance of using local ONNX models for embedding within the database?

A. Improved accuracy compared to external models
B. Reduced embedding dimensions for faster processing
C. Enhanced security because data remains within the database
D. Support for legacy SQL*Plus clients

Answer: C

Explanation:
Using local ONNX (Open Neural Network Exchange) models for embedding within Oracle Database 23ai means loading pre-trained models (e.g., via DBMS_VECTOR) into the database to generate vectors internally, rather than relying on external APIs or services. The primary significance is enhanced security (D): sensitive data (e.g., proprietary documents) never leaves the database, avoiding exposure to external networks or third-party providers. This aligns with enterprise needs for data privacy and compliance (e.g., GDPR), as the embedding process-say, converting "confidential report" to a vector-occurs within Oracle's secure environment, leveraging its encryption and access controls.
Option A (SQLPlus support) is irrelevant; ONNX integration is about AI functionality, not legacy client compatibility-SQLPlus can query vectors regardless. Option B (improved accuracy) is misleading; accuracy depends on the model's training, not its location-local vs. external models could be identical (e.g., same BERT variant). Option C (reduced dimensions) is a misconception; dimensionality is model-defined (e.g., 768 for BERT), not altered by locality-processing speed might improve due to reduced latency, but that's secondary. Security is the standout benefit, as Oracle's documentation emphasizes in-database processing to minimize data egress risks, a critical consideration for RAG or Select AI workflows where private data fuels LLMs. Without this, external calls could leak context, undermining trust in AI applications.

NEW QUESTION # 16
You are tasked with finding the closest matching sentences across books, where each book has multiple paragraphs and sentences. Which SQL structure should you use?

A. A nested query with ORDER BY
B. FETCH PARTITIONS BY clause
C. Exact similarity search with a single query vector
D. GROUP BY with vector operations

Answer: A

Explanation:
Finding the closest matching sentences across books involves comparing a query vector to sentence vectors stored in a table (e.g., columns: book_id, sentence, vector). A nested query with ORDER BY (A) is the optimal SQL structure: an inner query computes distances (e.g., SELECT sentence, VECTOR_DISTANCE(vector, :query_vector, COSINE) AS score FROM sentences), and the outer query sorts and limits results (e.g., SELECT * FROM (inner_query) ORDER BY score FETCH FIRST 5 ROWS ONLY). This ranks sentences by similarity, leveraging Oracle's vector capabilities efficiently, especially with an index.
Option B (exact search) describes a technique, not a structure, and a full scan is slow without indexing-lacking specificity here. Option C (GROUP BY) aggregates (e.g., by book), not ranks individual sentences, missing the "closest" goal. Option D (FETCH PARTITIONS BY) isn't a valid clause; it might confuse with IVF partitioning, but that's index-related, not query syntax. The nested structure allows flexibility (e.g., adding WHERE clauses) and aligns with Oracle's vector search examples, ensuring both correctness and scalability-crucial when books yield thousands of sentences.

NEW QUESTION # 17
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 (IVF) index
B. Hierarchical Navigable Small World (HNSW) index
C. Binary Tree (BT) index
D. Inverted File System (IFS) 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 # 18
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1Z0-184-25 RELIABLE DUMPS | 1Z0-184-25 PASS4SURE DUMPS PDF

1Z0-184-25 Reliable Dumps | 1Z0-184-25 Pass4sure Dumps Pdf