Oracle 1z0-1195-26 Exam Overview:
| Certification Vendor: | Oracle |
|---|---|
| Exam Name: | Oracle AI Database Foundations Associate |
| Exam Number: | 1Z0-1195-26 |
| Exam Price: | Free |
| Available Languages: | Japanese, English |
| Exam Duration: | 60 minutes |
| Certificate Validity Period: | 10 years |
| Real Exam Qty: | 40 |
| Passing Score: | 65% |
| Exam Format: | Multiple Choice, Online Proctored |
| Related Certifications: | Oracle AI Database Certified Foundations Associate Oracle Data Platform 2025 Certified Foundations Associate |
| Sample Questions: | Oracle 1z0-1195-26 Sample Questions |
| Exam Way: | Online proctored exam; Japanese delivery is also available. |
| Pre Condition: | No prerequisite exam or prior certification is required. |
| Official Syllabus URL: | https://www.oracle.com/education/certification/ |
Oracle 1z0-1195-26 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Building Low-Code Applications and Agentic AI | - Describe Oracle APEX as Oracle's low-code platform - Choose the appropriate Agent Factory capability for a no-code AI agent use case |
| Topic 2: 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 3: Working with AI and Vector Foundations | - 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 |
| Topic 4: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Create an Autonomous AI Database Serverless instance for a basic workload |
| Topic 5: Using Oracle Database Actions and Data Studio Tools | - Describe Database Actions and core development tools - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks |
| Topic 6: 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 |
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 graph view with ONE ROW PER STEP
- C. A JSON duality view with update restrictions
- D. A vector index with approximate search
Which example is a common AI use case?
- A. Rotating an encryption key on a fixed compliance schedule
- B. Listing database tables alphabetically in a schema browser
- C. Manually assigning a subnet CIDR during database provisioning
- D. Detecting fraudulent transactions from behavior patterns
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How does Oracle APEX help developers build applications with Oracle Database data?
- A. It is a graph analytics workspace for community detection
- B. It is primarily a command-line framework for database-only scripts
- C. It is browser-based and uses native SQL data access
- D. It is a separate application platform that cannot use Oracle Database data
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A business team wants to launch a no-code AI agent quickly. They prefer to start from a ready-made option and later refine the publishing workflow. Which Private Agent Factory capability path fits this need?
- A. Build a custom agent from a blank setup before reviewing any available templates
- B. Configure data-source connections before selecting a template or agent starting point
- C. Create a prompt-only prototype and publish it without using Agent Factory agent options
- D. Start with a pre-built agent or template, then use Agent Builder if more customization is needed
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Which pair correctly matches an AI domain to an example?
- A. Language - object detection in photos
- B. Vision - image classification
- C. Forecasting - entity extraction from text
- D. Speech - product demand prediction
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By Hale

