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Oracle 1z0-1195-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Using Oracle Database Actions and Data Studio Tools | 15% | - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks - Describe Database Actions and core development tools |
| Topic 2: Working with AI and Vector Foundations | 15% | - Describe AI, AGI, and machine learning foundations - Apply vector distance and indexing concepts to similarity search needs - Explain vectors, embeddings, and the Oracle VECTOR data type |
| Topic 3: Building Low-Code Applications and Agentic AI | 10% | - Describe Oracle APEX as Oracle's low-code platform - Choose the appropriate Agent Factory capability for a no-code AI agent use case |
| Topic 4: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy - Create an Autonomous AI Database Serverless instance for a basic workload - Describe Autonomous AI Database characteristics, offerings, and deployment choices |
| Topic 5: Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - 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 |
| Topic 6: Working with JSON and Graph in Oracle AI Database | 20% | - 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 |
Oracle AI Database Foundations Associate Sample Questions:
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. Return unfiltered database patches as the answer context.
- B. Replace all policy documents with graph edge IDs.
- C. Retrieve semantically similar document chunks to provide context for generation.
- D. Use only keyword matching because enterprise data must not use embeddings.
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What is the operational benefit of using a converged database instead of several point-solution databases?
- A. It can reduce operational complexity by using a more consistent security and patching approach
- B. It automatically removes the need to review security policies for database workloads
- C. It requires every application team to maintain a separate synchronization process
- D. It guarantees that all workloads use the same physical schema and access method
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Which set of characteristics is commonly used to describe Autonomous AI Database?
- A. Self-driving, self-securing, and self-repairing
- B. Self-coding, self-licensing, and self-networking
- C. Self-hosted, self-audited, and self-architected
- D. Self-training, self-upgrading, and self-documenting
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A team needs faster similarity search at scale and accepts approximate top-K results. Which feature should they use?
- A. A standard B-tree index optimized for exact match and range queries
- B. A graph view with ONE ROW PER STEP
- C. A JSON duality view with update restrictions
- D. A vector index with approximate search
In a property graph, what are vertices and edges?
- A. Vertices are storage partitions, and edges are encryption keys
- B. Vertices are JSON collections, and edges are maintenance policies
- C. Vertices represent entities, and edges represent relationships between them
- D. Vertices are path lengths and edges are SQL functions
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