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Oracle 1z0-1195-26 Exam Syllabus Topics:
Section
Objectives
Using Oracle Database Actions and Data Studio Tools
- Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks
- Describe Database Actions and core development tools
Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics
- Create an Autonomous AI Database Serverless instance for a basic workload
- Explain modern data characteristics and the Oracle AI Database 26ai converged strategy
- Describe Autonomous AI Database characteristics, offerings, and deployment choices
Working with AI and Vector Foundations
- Apply vector distance and indexing concepts to similarity search needs
- Describe AI, AGI, and machine learning foundations
- Explain vectors, embeddings, and the Oracle VECTOR data type
Working with JSON and Graph in Oracle AI Database
- Describe core graph concepts and graph analytic capabilities
- Distinguish when graph capabilities and Property Graph Views fit a business use case
- Explain JSON and Oracle AI Database JSON capabilities
Building Low-Code Applications and Agentic AI
- Choose the appropriate Agent Factory capability for a no-code AI agent use case
- Describe Oracle APEX as Oracle's low-code platform
Implementing Select AI and AI Vector Search in Autonomous AI Database
- Determine how AI Vector Search supports GenAI pipelines and RAG
- Describe Select AI in Autonomous AI Database
- Apply AI Vector Search to combined semantic and business-data search scenarios
Oracle AI Database Foundations Associate Sample Questions:
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A company must control the lifecycle of its database encryption keys to satisfy regulatory requirements. Which key management option should it use for the Oracle Autonomous AI Database instance?
A. Customer-managed keys stored through OCI Vault integration
B. Oracle-managed keys
C. APEX-managed workspace keys
D. Public internet certificates managed by Database Actions
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Modern applications often need to work with relational data, JSON documents, graph relationships, and vector embeddings. What challenge does using a different specialized database for each need create?
A. They can avoid all data transformation and synchronization work
B. They can query all data types together with one built-in model automatically
C. They can reduce administration because every platform shares one security model
D. They can create data silos that make integrated access and management harder
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discussion
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