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Oracle 1z0-1195-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| 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 |
| 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 |
| Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy - Create an Autonomous AI Database Serverless instance for a basic workload |
| Working with JSON and Graph in Oracle AI Database | 20% | - Distinguish when graph capabilities and Property Graph Views fit a business use case - Describe core graph concepts and graph analytic capabilities - Explain JSON and Oracle AI Database JSON capabilities |
| Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - Apply AI Vector Search to combined semantic and business-data search scenarios - Describe Select AI in Autonomous AI Database - 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:
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 create data silos that make integrated access and management harder
- D. They can reduce administration because every platform shares one security model
Correct Answer: C 🗳️
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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. Database Actions
- B. Property Graph View definition only
- C. Vector target accuracy settings
- D. Customer-managed key rotation
Correct Answer: A 🗳️
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What does Select AI enable in Autonomous AI Database?
- A. Administrators can rotate customer-managed encryption keys from a query prompt
- B. Users can ask natural-language questions to get data answers
- C. Users can replace every SQL query with graph pattern matching
- D. Administrators can provision database instances from natural-language prompts
Correct Answer: B 🗳️
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A business wants one data platform where semantic similarity, relational consistency, and SQL-based filtering all work together.
Which statement aligns with this design goal?
- A. Semantic search is usually handled after relational filters are removed from the query flow.
- B. Vector search requires data to be modeled as graph paths before SQL predicates are applied.
- C. A converged database can keep data consistent while combining vector search with SQL.
- D. SQL filtering is handled in a separate application step after vector search returns results.
Correct Answer: C 🗳️
In a RAG-style application, where does similarity search apply?
- A. It replaces the generation step by returning only raw vector values
- B. It creates the original source documents from the generated answer
- C. It retrieves the most relevant stored content for the user's question
- D. It evaluates user authentication rules before retrieving context
Correct Answer: C 🗳️
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