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Oracle 1z0-1195-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building Low-Code Applications and Agentic AI | 10% | - Choose the appropriate Agent Factory capability for a no-code AI agent use case - Describe Oracle APEX as Oracle's low-code platform |
| Working with AI and Vector Foundations | 15% | - Explain vectors, embeddings, and the Oracle VECTOR data type - Apply vector distance and indexing concepts to similarity search needs - Describe AI, AGI, and machine learning foundations |
| Using Oracle Database Actions and Data Studio Tools | 15% | - Describe Database Actions and core development tools - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks |
| Working with JSON and Graph in Oracle AI Database | 20% | - Explain JSON and Oracle AI Database JSON capabilities - Describe core graph concepts and graph analytic capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case |
| Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Create an Autonomous AI Database Serverless instance for a basic workload - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy |
| Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - Describe Select AI in Autonomous AI Database - Apply AI Vector Search to combined semantic and business-data search scenarios - Determine how AI Vector Search supports GenAI pipelines and RAG |
Oracle AI Database Foundations Associate Sample Questions:
What is OSON in Oracle AI Database JSON support?
- A. An optimized binary format for JSON storage
- B. A visualization tool for graph queries
- C. A SQL worksheet page in Database Actions
- D. A maintenance schedule for JSON collections
Correct Answer: A 🗳️
Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).
A company wants a GenAI assistant that answers policy questions by grounding responses in its internal documents.
Which use of AI Vector Search best supports this design?
- A. Replace all policy documents with graph edge IDs.
- B. Use only keyword matching because enterprise data must not use embeddings.
- C. Return unfiltered database patches as the answer context.
- D. Retrieve semantically similar document chunks to provide context for generation.
Correct Answer: D 🗳️
Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).
Which JSON feature helps represent repeating child data inside one document?
- A. A document identifier that points to an external table only
- B. A scalar property that stores one child value
- C. Arrays and nested objects
- D. A flat property list with each child stored as a separate document
Correct Answer: C 🗳️
Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).
Which set of characteristics is commonly used to describe Autonomous AI Database?
- A. Self-coding, self-licensing, and self-networking
- B. Self-driving, self-securing, and self-repairing
- C. Self-hosted, self-audited, and self-architected
- D. Self-training, self-upgrading, and self-documenting
Correct Answer: B 🗳️
Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).
What is an embedding in the context of AI Vector Search?
- A. A text-only backup of a JSON collection
- B. A graph edge label stored in a property graph
- C. A maintenance schedule attached to a database instance
- D. A sequence of numbers that represents semantic content
Correct Answer: D 🗳️
Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).
