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Oracle 1z0-1195-26 Exam Overview:
| Certification Vendor: | Oracle |
|---|---|
| Exam Name: | Oracle AI Database Foundations Associate |
| Exam Number: | 1Z0-1195-26 |
| Real Exam Qty: | 40 |
| Exam Duration: | 60 minutes |
| Exam Format: | Multiple Choice, Online Proctored |
| Exam Price: | Free |
| Passing Score: | 65% |
| Available Languages: | English, Japanese |
| Related Certifications: | Oracle AI Database Certified Foundations Associate Oracle Data Platform 2025 Certified Foundations Associate |
| Certificate Validity Period: | 10 years |
| 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 |
|---|---|
| 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 |
| Working with JSON and Graph in Oracle AI Database | - Explain JSON and Oracle AI Database JSON capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case - Describe core graph concepts and graph analytic capabilities |
| Implementing Select AI and AI Vector Search in Autonomous AI Database | - Describe Select AI 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 |
| 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 |
| 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 |
| 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 |
Oracle AI Database Foundations Associate Sample Questions:
In a property graph, what are vertices and edges?
- A. Vertices are storage partitions, and edges are encryption keys
- B. Vertices represent entities, and edges represent relationships between them
- C. Vertices are JSON collections, and edges are maintenance policies
- D. Vertices are path lengths and edges are SQL functions
Correct Answer: B 🗳️
Explanation: Only visible for TopExamCollection members. You can sign-up / login (it's free).
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. Retrieve semantically similar document chunks to provide context for generation.
- C. Use only keyword matching because enterprise data must not use embeddings.
- D. Return unfiltered database patches as the answer context.
Correct Answer: B 🗳️
Explanation: Only visible for TopExamCollection members. You can sign-up / login (it's free).
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 distance metric based on the embedding model that produced the vectors.
- B. Choose the metric based on the number of rows in the table.
- C. Choose the metric based on whether the data also has JSON attributes.
- D. Choose the metric based on the database maintenance schedule.
Correct Answer: A 🗳️
Explanation: Only visible for TopExamCollection members. You can sign-up / login (it's free).
Which set of characteristics is commonly used to describe Autonomous AI Database?
- A. Self-hosted, self-audited, and self-architected
- B. Self-driving, self-securing, and self-repairing
- C. Self-training, self-upgrading, and self-documenting
- D. Self-coding, self-licensing, and self-networking
Correct Answer: B 🗳️
Explanation: Only visible for TopExamCollection members. You can sign-up / login (it's free).
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
Correct Answer: A 🗳️
Explanation: Only visible for TopExamCollection members. You can sign-up / login (it's free).
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