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Oracle 1z0-1195-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Create an Autonomous AI Database Serverless instance for a basic workload - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy |
| Topic 2: Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - Determine how AI Vector Search supports GenAI pipelines and RAG - Apply AI Vector Search to combined semantic and business-data search scenarios - Describe Select AI in Autonomous AI Database |
| Topic 3: 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 |
| Topic 4: 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 |
| Topic 5: 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 |
| Topic 6: 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 |
Oracle AI Database Foundations Associate Sample Questions:
Which sequence matches a simple RAG pipeline?
- A. Generate an answer first, then create embeddings only if the answer looks weak
- B. Provision an application workspace, then skip retrieval and generate answers directly from schema metadata
- C. Build a graph model, then schedule maintenance, then create embeddings after generation
- D. Create embeddings, store vectors, retrieve relevant chunks by similarity, then generate an answer
Correct Answer: D 🗳️
Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).
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. Vector target accuracy settings
- B. Customer-managed key rotation
- C. Property Graph View definition only
- D. Database Actions
Correct Answer: D 🗳️
Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).
A data analyst wants to discover data, load it into the platform, and prepare it for later use without leaving the built-in tools. Which workspace fits this task?
- A. Property Graph View definition
- B. OCI Vault
- C. Data Studio
- D. VECTOR_DISTANCE function only
Correct Answer: C 🗳️
Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).
Which pair correctly matches an AI domain to an example?
- A. Language - object detection in photos
- B. Forecasting - entity extraction from text
- C. Vision - image classification
- D. Speech - product demand prediction
Correct Answer: C 🗳️
Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).
Which graph analytics capability is commonly used to rank important vertices based on their relationships?
- A. Vector distance metric
- B. PageRank
- C. Private endpoint access
- D. JSON Duality View
Correct Answer: B 🗳️
Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).
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