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Oracle 1z0-1195-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Create an Autonomous AI Database Serverless instance for a basic workload - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy |
| 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 JSON and Graph in Oracle AI Database | 20% | - Distinguish when graph capabilities and Property Graph Views fit a business use case - Explain JSON and Oracle AI Database JSON capabilities - Describe core graph concepts and graph analytic capabilities |
| Working with AI and Vector Foundations | 15% | - 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 |
| 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 |
| 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 |
Oracle AI Database Foundations Associate Sample Questions:
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 JSON duality view with update restrictions
- C. A vector index with approximate search
- D. A graph view with ONE ROW PER STEP
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. Replace job descriptions with graph edge labels only
- B. Use Early maintenance so results are ranked sooner
- C. Use one SQL query that combines relational filters with vector distance ordering
- D. Export all job postings to a separate vector platform and remove city filtering
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A developer wants one place to work with SQL, REST endpoints, JSON features, APEX, and machine learning tools. Which entry point should the developer use?
- A. Customer-managed key rotation
- B. Database Actions
- C. Vector target accuracy settings
- D. Property Graph View definition only
Explanation: Only visible for ValidExam members. You can sign-up / login (it's free).
In a property graph, what are vertices and edges?
- A. Vertices are JSON collections, and edges are maintenance policies
- B. Vertices are path lengths and edges are SQL functions
- C. Vertices are storage partitions, and edges are encryption keys
- D. Vertices represent entities, and edges represent relationships between them
How can developers create vectors for data objects in Oracle AI Database 26ai?
- A. By using a built-in vectorization function
- B. By loading the data into a separate vector service before querying it
- C. By defining every vector as a JSON document manually
- D. By converting all source objects into Property Graph Views first
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