Exam Name: Oracle AI Database Foundations Associate
Certification Provider: Oracle
Corresponding Certification: Oracle Cloud Infrastructure
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Oracle 1z0-1195-26 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Implementing Select AI and AI Vector Search in Autonomous AI Database | - Describe Select AI in Autonomous AI Database - Determine how AI Vector Search supports GenAI pipelines and RAG - Apply AI Vector Search to combined semantic and business-data search scenarios |
| Using Oracle Database Actions and Data Studio Tools | - Describe Database Actions and core development tools - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks |
| Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy - Create an Autonomous AI Database Serverless instance for a basic workload |
| Building Low-Code Applications and Agentic AI | - Describe Oracle APEX as Oracle's low-code platform - Choose the appropriate Agent Factory capability for a no-code AI agent use case |
| Working with JSON and Graph in Oracle AI Database | - Distinguish when graph capabilities and Property Graph Views fit a business use case - Describe core graph concepts and graph analytic capabilities - Explain JSON and Oracle AI Database JSON capabilities |
| Working with AI and Vector Foundations | - Describe AI, AGI, and machine learning foundations - Explain vectors, embeddings, and the Oracle VECTOR data type - Apply vector distance and indexing concepts to similarity search needs |
Oracle AI Database Foundations Associate Sample Questions:
Which task is a common use of graph analytics?
- A. Sorting records only by a single numeric column value
- B. Storing each relationship as an isolated text value with no links
- C. Listing unrelated records without evaluating relationships
- D. Determining communities or connected clusters in a network
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When comparing a query vector with stored vectors, what does a smaller vector distance indicate?
- A. The items are more semantically similar
- B. The items require a larger maintenance window
- C. The query must switch from SQL to GraphQL
- D. The stored vectors were created with different models
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A data engineer is creating a vector similarity query and wants to choose the distance metric correctly.
Which guidance should be applied?
- A. Choose the metric based on whether the data also has JSON attributes.
- B. Choose the metric based on the number of rows in the table.
- C. Choose the metric based on the database maintenance schedule.
- D. Choose the distance metric based on the embedding model that produced the vectors.
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A retailer wants to find products semantically similar to a shopper's description, but only from items that are in stock and sold in the shopper's region. Which design meets this requirement?
- A. Use a single SQL statement that combines stock and region filters with vector ranking
- B. Use JSON Duality Views because similarity search requires document-only access
- C. Use vector search only, then manually remove unavailable products outside the database
- D. Use a property graph because vector search cannot work with business predicates
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In a RAG-style application, where does similarity search apply?
- A. It creates the original source documents from the generated answer
- B. It replaces the generation step by returning only raw vector values
- C. It retrieves the most relevant stored content for the user's question
- D. It evaluates user authentication rules before retrieving context
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