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Oracle 1z0-1195-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Management and Oracle Data Platform Overview | ~11% | - Data management concepts and data types - Oracle Data Strategy and multi-cloud deployment models - Modern data platform value and architecture |
| MySQL HeatWave and NoSQL Services | ~11% | - Oracle NoSQL Database features and use cases - MySQL HeatWave architecture and analytics |
| Security, Resilience, and Cloud Integration | ~21% | - Cloud-native database services and deployment strategies - High availability, backup, and disaster recovery - Database security architectures and data protection |
| Autonomous AI Database and Tools | ~16% | - Shared vs dedicated infrastructure - Core features of Autonomous AI Database - Built-in management and query tools |
| Oracle Machine Learning and AI Integration | ~15% | - In-database machine learning algorithms - Oracle Data Studio and visualization - AI agents and LLM integration |
| Oracle Database Services — Exadata, DBCS, and Engineered Systems | ~11% | - Database Cloud Service (DBCS) characteristics - Exadata architecture and features |
| Converged Database — Multi-Model and AI Capabilities | ~15% | - Oracle AI Vector Search concepts - Select AI and natural language querying - JSON, Graph, Spatial, and key-value data support |
Oracle AI Database Foundations Associate Sample Questions:
Question #1
Which set of characteristics is commonly used to describe Autonomous AI Database?
A. Self-training, self-upgrading, and self-documenting
B. Self-coding, self-licensing, and self-networking
C. Self-hosted, self-audited, and self-architected
D. Self-driving, self-securing, and self-repairing
Question #2
A support portal must search product manuals semantically while also filtering results by product line and support level stored in relational columns. How does Oracle AI Vector Search support this requirement?
A. It supports semantic search with relational business filters in an integrated database approach
B. It stores structured filter values as graph relationships before search can run
C. It uses document APIs for search while excluding SQL-based business predicates
D. It requires semantic search results to be filtered outside the database after retrieval
Question #3
In a RAG-style application, where does similarity search apply?
A. It evaluates user authentication rules before retrieving context
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 creates the original source documents from the generated answer
Question #4
A recruiting application must find the top job postings that semantically match a candidate's resume, but only in the candidate's city. How should the application meet this requirement?
A. Use one SQL query that combines relational filters with vector distance ordering
B. Replace job descriptions with graph edge labels only
C. Export all job postings to a separate vector platform and remove city filtering
D. Use Early maintenance so results are ranked sooner
Question #5
Which output can Select AI deliver to an application?
A. A maintenance recommendation generated from patch history
B. A graph visualization generated from schema relationships
C. A result set, a narrative, or the SQL
D. A JSON export file generated from a collection table
Solutions:
| Question #1 Answer: D | Question #2 Answer: A | Question #3 Answer: C | Question #4 Answer: A | Question #5 Answer: C |







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