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Oracle 1z0-1195-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Working with AI and Vector Foundations | 15% | - Explain vectors, embeddings, and the Oracle VECTOR data type - Describe AI, AGI, and machine learning foundations - Apply vector distance and indexing concepts to similarity search needs |
| 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 |
| 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 |
| 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 |
| 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 |
| Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - Apply AI Vector Search to combined semantic and business-data search scenarios - Determine how AI Vector Search supports GenAI pipelines and RAG - Describe Select AI in Autonomous AI Database |
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. Replace all policy documents with graph edge IDs.
- B. Return unfiltered database patches as the answer context.
- 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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How can developers create vectors for data objects in Oracle AI Database 26ai?
- A. By loading the data into a separate vector service before querying it
- B. By defining every vector as a JSON document manually
- C. By using a built-in vectorization function
- D. By converting all source objects into Property Graph Views first
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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. APEX-managed workspace keys
- B. Customer-managed keys stored through OCI Vault integration
- C. Oracle-managed keys
- D. Public internet certificates managed by Database Actions
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Which graph analytics capability is commonly used to rank important vertices based on their relationships?
- A. Private endpoint access
- B. Vector distance metric
- C. PageRank
- D. JSON Duality View
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A retailer wants to find products semantically similar to a shopper's description, but only from items that are in stock and sold in the shopper's region. Which design meets this requirement?
- A. Use a single SQL statement that combines stock and region filters with vector ranking
- B. Use JSON Duality Views because similarity search requires document-only access
- C. Use a property graph because vector search cannot work with business predicates
- D. Use vector search only, then manually remove unavailable products outside the database
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