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Updated: Sep 08, 2026
No. of Questions: 54 Questions & Answers with Testing Engine
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| Certification Vendor: | Oracle |
|---|---|
| Exam Name: | Oracle AI Database Foundations Associate |
| Exam Number: | 1Z0-1195-26 |
| Exam Duration: | 60 minutes |
| Real Exam Qty: | 40 |
| Related Certifications: | Oracle AI Database Certified Foundations Associate Oracle Data Platform 2025 Certified Foundations Associate |
| Certificate Validity Period: | 10 years |
| Exam Price: | Free |
| Available Languages: | English, Japanese |
| Passing Score: | 65% |
| Exam Format: | Multiple Choice, Online Proctored |
| 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/ |
| 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: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | - 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 4: 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 5: 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 6: Implementing Select AI and AI Vector Search in Autonomous AI Database | - Determine how AI Vector Search supports GenAI pipelines and RAG - Describe Select AI in Autonomous AI Database - Apply AI Vector Search to combined semantic and business-data search scenarios |
Question 1
A manufacturer needs graph analysis that reflects inserts and updates from operational tables immediately.
How does a Property Graph View support this requirement?
A. It uses metadata definitions so graph queries read current data from referenced objects
B. It converts source tables into JSON collections before graph queries can run
C. It provides graph visualization while requiring a separate platform for graph query execution
D. It stores a separate graph copy that is refreshed only during scheduled batch jobs
Question 2
Which offering is positioned for JSON-centric workloads and includes Oracle Database API for MongoDB?
A. Autonomous AI Transaction Processing only
B. Autonomous AI JSON Database
C. Autonomous AI Lakehouse
D. Oracle APEX Service
Question 3
A data engineer is creating a vector similarity query and wants to choose the distance metric correctly.
Which guidance should be applied?
A. Choose the metric based on whether the data also has JSON attributes.
B. Choose the distance metric based on the embedding model that produced the vectors.
C. Choose the metric based on the number of rows in the table.
D. Choose the metric based on the database maintenance schedule.
Question 4
A team already uses Database Actions and now needs the part of the environment focused on discovery, load, integration, and cataloging. Which workspace should they open?
A. Graph Studio
B. Data Studio
C. SQL Worksheet
D. Database Users
Question 5
When comparing a query vector with stored vectors, what does a smaller vector distance indicate?
A. The stored vectors were created with different models
B. The items are more semantically similar
C. The items require a larger maintenance window
D. The query must switch from SQL to GraphQL
Solutions:
| Question 1 Answer: A | Question 2 Answer: B | Question 3 Answer: B | Question 4 Answer: B | Question 5 Answer: B |
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