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Oracle 1z0-1195-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: MySQL HeatWave and NoSQL Services | ~11% | - MySQL HeatWave architecture and analytics - Oracle NoSQL Database features and use cases |
| Topic 2: Oracle Machine Learning and AI Integration | ~15% | - In-database machine learning algorithms - Oracle Data Studio and visualization - AI agents and LLM integration |
| Topic 3: Oracle Database Services — Exadata, DBCS, and Engineered Systems | ~11% | - Database Cloud Service (DBCS) characteristics - Exadata architecture and features |
| Topic 4: Converged Database — Multi-Model and AI Capabilities | ~15% | - Oracle AI Vector Search concepts - JSON, Graph, Spatial, and key-value data support - Select AI and natural language querying |
| Topic 5: Data Management and Oracle Data Platform Overview | ~11% | - Data management concepts and data types - Modern data platform value and architecture - Oracle Data Strategy and multi-cloud deployment models |
| Topic 6: Security, Resilience, and Cloud Integration | ~21% | - Cloud-native database services and deployment strategies - High availability, backup, and disaster recovery - Database security architectures and data protection |
| Topic 7: Autonomous AI Database and Tools | ~16% | - Built-in management and query tools - Shared vs dedicated infrastructure - Core features of Autonomous AI Database |
Oracle AI Database Foundations Associate Sample Questions:
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 metric based on the database maintenance schedule.
- C. Choose the metric based on the number of rows in the table.
- D. Choose the distance metric based on the embedding model that produced the vectors.
Explanation: Only visible for TorrentExam members. You can sign-up / login (it's free).
A business wants one data platform where semantic similarity, relational consistency, and SQL-based filtering all work together.
Which statement aligns with this design goal?
- A. SQL filtering is handled in a separate application step after vector search returns results.
- B. Vector search requires data to be modeled as graph paths before SQL predicates are applied.
- C. A converged database can keep data consistent while combining vector search with SQL.
- D. Semantic search is usually handled after relational filters are removed from the query flow.
What does the Oracle VECTOR data type enable?
- A. Automatic conversion of every table into a graph model
- B. Automatic JSON schema validation for all APIs
- C. Browser-based low-code page design
- D. Native storage of vector values in the database
Explanation: Only visible for TorrentExam members. You can sign-up / login (it's free).
How is Oracle APEX described?
- A. Oracle's encryption key lifecycle manager
- B. Oracle's dedicated graph-only engine
- C. Oracle's JSON binary storage format
- D. Oracle's low-code development platform
Explanation: Only visible for TorrentExam members. You can sign-up / login (it's free).
A business wants one data platform where semantic similarity, relational consistency, and SQL-based filtering all work together.
Which statement aligns with this design goal?
- A. SQL filtering is handled in a separate application step after vector search returns results.
- B. Vector search requires data to be modeled as graph paths before SQL predicates are applied.
- C. A converged database can keep data consistent while combining vector search with SQL.
- D. Semantic search is usually handled after relational filters are removed from the query flow.
Explanation: Only visible for TorrentExam members. You can sign-up / login (it's free).
