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Oracle 1z0-1195-26 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Implementing Select AI and AI Vector Search 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 - Describe Select AI in Autonomous AI Database |
| Topic 2: Working with AI and Vector Foundations | - 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 |
| Topic 3: 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 |
| Topic 4: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | - 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 |
| Topic 5: Working with JSON and Graph in Oracle AI Database | - 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 |
| Topic 6: 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 |
Oracle AI Database Foundations Associate Sample Questions:
Which set of tools is specifically highlighted as available from Database Actions?
- A. Maintenance scheduling, private endpoint creation, and customer key rotation
- B. OCI Vault, IAM policy editor, billing reports, and tenancy limits
- C. SQL worksheet, data modeler, APEX, machine learning, REST, and JSON tools
- D. Compute instance launch tools, block volume tools, and load balancer tools
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
What is an embedding in the context of AI Vector Search?
- A. A graph edge label stored in a property graph
- B. A maintenance schedule attached to a database instance
- C. A sequence of numbers that represents semantic content
- D. A text-only backup of a JSON collection
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
What happens after a user asks a business question with Select AI?
- A. The database disables SQL and switches to document APIs only
- B. The platform manages prompt engineering, interacts with an LLM, and produces the answer
- C. The system selects database tables and joins without using any schema metadata
- D. The user must manually create embeddings before SQL can be generated
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
A team needs faster similarity search at scale and accepts approximate top-K results. Which feature should they use?
- A. A graph view with ONE ROW PER STEP
- B. A vector index with approximate search
- C. A standard B-tree index optimized for exact match and range queries
- D. A JSON duality view with update restrictions
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
A bank wants to identify indirect money movement patterns across several accounts to support fraud investigation. Which approach fits this requirement?
- A. Use a relational report that shows one transaction at a time without link analysis
- B. Store only summary totals for each account without relationship data
- C. Store transaction descriptions as flat text and search by keywords only
- D. Model accounts as vertices and transfers as edges in a property graph
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
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