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| Section | Weight | Objectives |
|---|---|---|
| Working with AI and Vector Foundations | 15% | - Explain vectors, embeddings, and the Oracle VECTOR data type - Apply vector distance and indexing concepts to similarity search needs - Describe AI, AGI, and machine learning foundations |
| Working with JSON and Graph in Oracle AI Database | 20% | - Describe core graph concepts and graph analytic capabilities - Explain JSON and Oracle AI Database JSON capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case |
| Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Create an Autonomous AI Database Serverless instance for a basic workload - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy - Describe Autonomous AI Database characteristics, offerings, and deployment choices |
| Building Low-Code Applications and Agentic AI | 10% | - Describe Oracle APEX as Oracle's low-code platform - Choose the appropriate Agent Factory capability for a no-code AI agent use case |
| Using Oracle Database Actions and Data Studio Tools | 15% | - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks - Describe Database Actions and core development tools |
| 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 |
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NEW QUESTION # 18
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?
Answer: B
Explanation:
Database Actions is the correct centralized entry point because it is the browser-based development and administration environment bundled with Autonomous AI Database. Oracle documents Database Actions as providing development, data, administration, monitoring, and download capabilities. Its Development area includes SQL, Data Modeler, REST, JSON, Oracle Machine Learning, Graph Studio, and Oracle APEX, which directly matches the developer's requirement for a single integrated workspace. The SQL worksheet supports SQL and PL/SQL development, while REST and JSON tools expose database data through modern application interfaces. APEX provides low-code application development, and Oracle Machine Learning integrates analytical and machine-learning workflows with database-resident data.
The other choices are individual configuration concepts rather than comprehensive developer entry points.
Customer-managed key rotation relates to encryption-key lifecycle management; vector target accuracy controls approximate vector-search behavior; and a Property Graph View is a graph modeling construct.
Therefore, none provides the breadth of tooling required. The uploaded question set likewise identifies Database Actions as the correct response.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Database Actions development tools and integrated workspaces.
NEW QUESTION # 19
In a property graph, what are vertices and edges?
Answer: B
NEW QUESTION # 20
Which graph analytics capability is commonly used to rank important vertices based on their relationships?
Answer: B
Explanation:
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
PageRank is the graph-analytics algorithm intended to measure the relative importance of vertices based on graph relationships. Oracle's property-graph documentation describes PageRank as ranking vertices by considering incoming neighbors and the importance of those neighbors. This makes it appropriate for identifying influential or significant entities in connected data, such as important web pages, accounts, people, devices, or other nodes. Private endpoint access is a networking feature, JSON Duality View is a relational-to- JSON representation mechanism, and a vector distance metric measures similarity between vector embeddings. None of those performs graph centrality ranking. Therefore, PageRank is the only option that directly satisfies the requirement to rank vertices according to their relationships. This belongs under
"Working with JSON and Graph in Oracle AI Database." Oracle Docs
NEW QUESTION # 21
Which set of characteristics is commonly used to describe Autonomous AI Database?
Answer: C
Explanation:
The uploaded question set identifies self-driving, self-securing, and self-repairing as the defining Autonomous AI Database characteristics. Oracle documentation uses the same terminology when describing Autonomous Database services.
Self-driving refers to automated database-management activities that traditionally require significant DBA intervention, including provisioning, tuning, optimization, backups, patching, and scaling. Self-securing encompasses automated security practices designed to protect database infrastructure and data, including encryption and security maintenance. Self-repairing describes automated availability and fault-management capabilities intended to reduce downtime and recover from infrastructure or database failures with minimal manual involvement.
These attributes are central to Oracle's Autonomous Database strategy because the service shifts routine infrastructure and database operations from customer-managed procedures toward automated cloud-service capabilities. This allows development and data teams to focus on application logic and business workloads rather than routine database administration.
The alternative combinations are not Oracle's established characterization. Autonomous AI Database does not promise autonomous application coding, licensing, documentation creation, or architectural design. Those terms incorrectly broaden the scope of database automation beyond the capabilities Oracle associates with the Autonomous platform.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - Autonomous characteristics and automated database operations.
NEW QUESTION # 22
A development team wants to receive patches before the regular maintenance schedule so they can validate changes early.
Which maintenance option should they select?
Answer: C
Explanation:
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Oracle Autonomous AI Database supports Regular and Early patch levels or maintenance schedules. The Early option applies patches before the Regular schedule so development and test systems can validate upcoming changes before production systems receive them. Oracle's current documentation states that Early patches are applied one week before the Regular scheduled patch and explicitly recommends Early for development and test databases when organizations want advance validation. Regular follows the normal maintenance cycle. "Late maintenance" and "Application-controlled maintenance" are not the applicable patch-level choices for this Serverless scenario. Because the team specifically wants patches before the regular schedule for early validation, the correct selection is Early maintenance. This falls under Autonomous AI Database operational basics, maintenance, and patch-management concepts. Oracle Docs
NEW QUESTION # 23
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