Exam overview

Oracle 1Z0-184-25 Exam Dumps

Vendor

Oracle

Exam Code

1Z0-184-25

Actual Exam Duration

90 Minutes

TOTAL QUESTIONS

Exam Name

Oracle Database AI Vector Search Professional

Purchase

$ 40

One-time payment • Instant access

Exam practice

Exam Q&A

Select an option, then click Show Answer.

Q1:
What is the significance of using local ONNX models for embedding within the database?

A: Support for legacy SQL*Plus clients

B: Improved accuracy compared to external models

C: Reduced embedding dimensions for faster processing

D: Enhanced security because data remains within the database

Answer

Correct Answer: D

Q2:
You want to quickly retrieve the top-10 matches for a query vector from a dataset of billions of vectors, prioritizing speed over exact accuracy. What is the best approach?

A: Exact similarity search using flat search

B: Approximate similarity search with a low target accuracy setting

C: Relational filtering combined with an exact search

D: Exact similarity search with a high target accuracy setting

Answer

Correct Answer: B

Q3:
Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?

A: It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries

B: 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

C: 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

D: 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

Answer

Correct Answer: A

Q4:
You are working with vector search in Oracle Database 23ai and need to ensure the integrity of your vector data during storage and retrieval. Which factor is crucial for maintaining the accuracy and reliability of your vector search results?

A: Using the same embedding model for both vector creation and similarity search

B: Regularly updating vector embeddings to reflect changes in the source data

C: The specific distance algorithm employed for vector comparisons

D: The physical storage location of the vector data

Answer

Correct Answer: A

Q5:
Which operation is NOT permitted on tables containing VECTOR columns?

A: SELECT

B: UPDATE

C: DELETE

D: JOIN ON VECTOR columns

Answer

Correct Answer: D

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Exam overview

Oracle 1Z0-184-25 Exam Dumps

Purchase

$ 40

One-time payment • Instant access

Exam practice

Exam Q&A

Q1:
What is the significance of using local ONNX models for embedding within the database?

A: Support for legacy SQL*Plus clients

B: Improved accuracy compared to external models

C: Reduced embedding dimensions for faster processing

D: Enhanced security because data remains within the database

Q2:
You want to quickly retrieve the top-10 matches for a query vector from a dataset of billions of vectors, prioritizing speed over exact accuracy. What is the best approach?

A: Exact similarity search using flat search

B: Approximate similarity search with a low target accuracy setting

C: Relational filtering combined with an exact search

D: Exact similarity search with a high target accuracy setting

Q3:
Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?

A: It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries

B: 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

C: 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

D: 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

Q4:
You are working with vector search in Oracle Database 23ai and need to ensure the integrity of your vector data during storage and retrieval. Which factor is crucial for maintaining the accuracy and reliability of your vector search results?

A: Using the same embedding model for both vector creation and similarity search

B: Regularly updating vector embeddings to reflect changes in the source data

C: The specific distance algorithm employed for vector comparisons

D: The physical storage location of the vector data

Q5:
Which operation is NOT permitted on tables containing VECTOR columns?

A: SELECT

B: UPDATE

C: DELETE

D: JOIN ON VECTOR columns

- Testimonials -

Real Results From Real Students

Frequently Asked Question (FAQ's)

Useful Links

Extra Links

Exam overview

Oracle 1Z0-184-25 Exam Dumps

Purchase

$ 40

One-time payment • Instant access

Exam practice

Exam Q&A

Q1: What is the significance of using local ONNX models for embedding within the database?

A: Support for legacy SQL*Plus clients

B: Improved accuracy compared to external models

C: Reduced embedding dimensions for faster processing

D: Enhanced security because data remains within the database

Q2: You want to quickly retrieve the top-10 matches for a query vector from a dataset of billions of vectors, prioritizing speed over exact accuracy. What is the best approach?

A: Exact similarity search using flat search

B: Approximate similarity search with a low target accuracy setting

C: Relational filtering combined with an exact search

D: Exact similarity search with a high target accuracy setting

Q3: Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?

A: It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries

B: 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

C: 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

D: 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

Q4: You are working with vector search in Oracle Database 23ai and need to ensure the integrity of your vector data during storage and retrieval. Which factor is crucial for maintaining the accuracy and reliability of your vector search results?

A: Using the same embedding model for both vector creation and similarity search

B: Regularly updating vector embeddings to reflect changes in the source data

C: The specific distance algorithm employed for vector comparisons

D: The physical storage location of the vector data

Q5: Which operation is NOT permitted on tables containing VECTOR columns?

A: SELECT

B: UPDATE

C: DELETE

D: JOIN ON VECTOR columns

- Testimonials -

Real Results From Real Students

Frequently Asked Question (FAQ's)

Q1:
What is the significance of using local ONNX models for embedding within the database?

Q2:
You want to quickly retrieve the top-10 matches for a query vector from a dataset of billions of vectors, prioritizing speed over exact accuracy. What is the best approach?

Q3:
Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?

Q4:
You are working with vector search in Oracle Database 23ai and need to ensure the integrity of your vector data during storage and retrieval. Which factor is crucial for maintaining the accuracy and reliability of your vector search results?

Q5:
Which operation is NOT permitted on tables containing VECTOR columns?