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Oracle 1Z0-184-25 Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 2	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.
Thema 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.
Thema 4	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.

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Oracle AI Vector Search Professional 1Z0-184-25 Prüfungsfragen mit Lösungen (Q32-Q37):

32. Frage
What happens when you attempt to insert a vector with an incorrect number of dimensions into a VECTOR column with a defined number of dimensions?

A. The database pads the vector with zeros to match the defined dimensions
B. The database truncates the vector to fit the defined dimensions
C. The database ignores the defined dimensions and inserts the vector as is
D. The insert operation fails, and an error message is thrown

Antwort: D

Begründung:
In Oracle Database 23ai, a VECTOR column with a defined dimension count (e.g., VECTOR(4, FLOAT32)) enforces strict dimensional integrity to ensure consistency for similarity search and indexing. Attempting to insert a vector with a mismatched number of dimensions-say, TO_VECTOR('[1.2, 3.4, 5.6]') (3D) into a VECTOR(4)-results in the insert operation failing with an error (D), such as ORA-13199: "vector dimension mismatch." This rigidity protects downstream AI operations; a 3D vector in a 4D column would misalign with indexed data (e.g., HNSW graphs), breaking similarity calculations like cosine distance, which require uniform dimensionality.
Option A (truncation) is tempting but incorrect; Oracle doesn't silently truncate [1.2, 3.4, 5.6] to [1.2, 3.4]-this would discard data arbitrarily, risking semantic loss (e.g., a truncated sentence embedding losing meaning). Option B (padding with zeros) seems plausible-e.g., [1.2, 3.4, 5.6] becoming [1.2, 3.4, 5.6, 0]-but Oracle avoids implicit padding to prevent unintended semantic shifts (zero-padding could alter distances). Option C (ignoring dimensions) only applies to undefined VECTOR columns (e.g., VECTOR without size), not fixed ones; here, the constraint is enforced. The failure (D) forces developers to align data explicitly (e.g., regenerate embeddings), ensuring reliability-a strict but necessary design choice in Oracle's AI framework. In practice, this error prompts debugging upstream data pipelines, avoiding silent failures that could plague production AI systems.

33. Frage
Which function is used to generate vector embeddings within an Oracle database?

A. DBMS_VECTOR_CHAIN.UTL_TO_TEXT
B. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS
C. DBMS_VECTOR_CHAIN.UTL_TO_GENERATE_TEXT
D. DBMS_VECTOR_CHAIN.UTL_TO_EMBEDDINGS

Antwort: D

Begründung:
In Oracle 23ai, the DBMS_VECTOR_CHAIN package provides utilities for vector workflows. UTL_TO_EMBEDDINGS (C) generates vector embeddings from text within the database, typically using an ONNX model, supporting RAG and search applications. UTL_TO_CHUNKS (A) splits text, not generates embeddings. UTL_TO_TEXT (B) converts documents to text, a preprocessing step. UTL_TO_GENERATE_TEXT (D) doesn't exist; text generation is handled by LLMs, not this package. Oracle's documentation identifies UTL_TO_EMBEDDINGS as the embedding creation function in PL/SQL workflows.

34. Frage
Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?

A. OCI Data Science
B. OCI Generative AI
C. OCI Language
D. OCI Vision

Antwort: B

Begründung:
Select AI in Oracle Database 23ai integrates with OCI Generative AI (B) to process natural language queries and generate context-aware responses using large language models (LLMs). OCI Language (A) focuses on text analysis (e.g., sentiment, entity recognition), not generative tasks. OCI Vision (C) handles image processing, unrelated to Select AI's text-based functionality. OCI Data Science (D) supports model development, not direct integration with Select AI. Oracle's documentation explicitly names OCI Generative AI as the integrated service for Select AI's LLM capabilities.

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

Antwort: B

Begründung:
IVF (Inverted File) indexes in Oracle 23ai partition vectors into clusters, probing a subset during queries for efficiency. Incomplete results suggest insufficient partitions are probed, reducing recall. The TARGET_ACCURACY clause (A) allows users to specify a desired accuracy percentage (e.g., 90%), dynamically increasing the number of probed partitions to meet this target, thus improving accuracy at the cost of latency. Switching to HNSW (B) offers higher accuracy but requires re-indexing and may not be necessary if IVF tuning suffices. Increasing VECTOR_MEMORY_SIZE (C) allocates more memory for vector operations but doesn't directly affect probe count. EFCONSTRUCTION (D) is an HNSW parameter, irrelevant to IVF. Oracle's IVF documentation highlights TARGET_ACCURACY as the recommended tuning mechanism.

36. Frage
Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?

A. It allows users to train their own specialized LLMs directly within the Oracle Database environment using their internal data, thereby reducing reliance on external AI providers
B. It enables Large Language Models (LLMs) to access and process real-time data streams from diverse sources to generate the most up-to-date insights
C. It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries
D. It primarily aims to optimize the performance and efficiency of LLMs by using advanced data retrieval techniques, thus minimizing response times and reducing computational overhead

Antwort: C

Begründung:
RAG in Oracle Database 23ai combines vector search with LLMs to enhance responses by retrieving relevant private data from the database (e.g., via VECTOR columns) and augmenting LLM prompts. This (A) improves context-awareness and precision, leveraging enterprise-specific data without retraining LLMs. Optimizing LLM performance (B) is a secondary benefit, not the core focus. Training specialized LLMs (C) is not RAG's purpose; it uses existing models. Real-time streaming (D) is possible but not the primary benefit, as RAG focuses on stored data retrieval. Oracle's RAG documentation emphasizes private data integration for better LLM outputs.

37. Frage
......

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1Z0-184-25 Aktuelle Prüfung - 1Z0-184-25 Prüfungsguide & 1Z0-184-25 Praxisprüfung

Oracle 1Z0-184-25 Prüfungsplan:

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Oracle AI Vector Search Professional 1Z0-184-25 Prüfungsfragen mit Lösungen (Q32-Q37):

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