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| Section | Weight | Objectives |
|---|---|---|
| Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - 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 |
| 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 |
| Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - 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 |
| 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 |
| Working with AI and Vector Foundations | 15% | - Describe AI, AGI, and machine learning foundations - Explain vectors, embeddings, and the Oracle VECTOR data type - Apply vector distance and indexing concepts to similarity search needs |
| 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 |
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NEW QUESTION # 31
A company must control the lifecycle of its database encryption keys to satisfy regulatory requirements.
Which key management option should it use for the Oracle Autonomous AI Database instance?
Answer: A
Explanation:
Customer-managed encryption keys integrated with OCI Vault are appropriate when an organization requires direct control over encryption-key lifecycle operations for security, governance, or regulatory compliance.
Autonomous AI Database uses Transparent Data Encryption to protect database data and supports both Oracle-managed and customer-managed master encryption keys. With the default Oracle-managed approach, Oracle performs key-management operations. With customer-managed keys, the organization creates and manages a master key in a supported external key-management system such as OCI Vault.
OCI Vault centralizes secure key storage and enables the customer to control operations such as key creation, rotation, lifecycle governance, access policy, and auditing. Autonomous AI Database then uses the customer- managed master encryption key as part of the TDE key hierarchy. This directly addresses the stated requirement for organizational control of encryption keys. Public certificates are intended for network identity and TLS-related functions rather than TDE key lifecycle management. APEX workspace configuration is unrelated to database master encryption keys. Oracle-managed keys provide strong encryption but do not satisfy a requirement specifically calling for customer-controlled lifecycle management.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - Autonomous AI Database security, TDE, OCI Vault, and customer-managed encryption keys.
NEW QUESTION # 32
A manufacturer needs graph analysis that reflects inserts and updates from operational tables immediately.
How does a Property Graph View support this requirement?
Answer: A
Explanation:
A Property Graph View provides a graph interpretation directly over data stored in relational database tables rather than requiring a separately maintained copy of that data. Oracle documents the Property Graph View as a view-like object containing metadata describing vertices, edges, labels, keys, and properties. Because the underlying graph information remains in the referenced relational tables, modifications to those tables are immediately reflected when the graph is queried.
This architecture directly satisfies the manufacturer's requirement. When operational applications insert or update rows in the underlying objects, graph queries subsequently operate against the updated relational data through the graph metadata definition. There is no required intermediate conversion to JSON and no scheduled batch refresh of a separate graph copy. Oracle also supports direct graph queries against database- resident property graph structures, including pattern matching through database graph-query facilities.
A separate in-memory graph server can be used for certain advanced analytics scenarios, but that does not change what a database Property Graph View fundamentally provides. The key exam distinction is metadata- based graph representation over existing database objects versus duplicated graph storage requiring synchronization. Therefore, option D precisely matches the required real-time operational behavior.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - Property Graph Views, metadata-based graph modeling, and relational-table integration.
NEW QUESTION # 33
Which graph analytics capability is commonly used to rank important vertices based on their relationships?
Answer: A
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 # 34
What is OSON in Oracle AI Database JSON support?
Answer: B
Explanation:
OSON is Oracle's optimized binary representation for JSON data. Oracle AI Database uses OSON as the native storage representation of the SQL JSON data type. Unlike textual JSON stored in VARCHAR2, CLOB, or BLOB values, native JSON data does not need to be repeatedly parsed from character representation for common processing operations. Oracle states that OSON is optimized for fast query and update operations in both the Oracle AI Database server and supported database clients.
The practical advantage is that applications retain JSON's flexible document model while gaining database- native processing efficiency, SQL integration, indexing capabilities, and transactional control. OSON therefore concerns the physical/optimized representation of JSON data, not the operational scheduling of JSON collections or the visualization of graph structures. It is also unrelated to the SQL Worksheet, which is a Database Actions development interface for executing SQL and PL/SQL. The uploaded question explicitly identifies "An optimized binary format for JSON storage" as the correct answer.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - native JSON data type, OSON binary JSON representation, JSON query and update processing.
NEW QUESTION # 35
How can developers access JSON Duality Views?
Answer: C
Explanation:
Developers can access JSON-Relational Duality Views using document-oriented interfaces, including Oracle AI Database API for MongoDB , as well as SQL-based database interfaces. The uploaded source explicitly identifies "MongoDB-compatible APIs or SQL" as correct. Oracle documentation confirms that duality views expose relational table data as JSON documents and that applications can interact with the same underlying data either document-centrically or relationally.
With the MongoDB-compatible API, the duality view can be treated as a document collection by applications using familiar MongoDB drivers and development patterns. At the same time, because the authoritative data remains in Oracle relational tables, SQL and other relational capabilities can operate directly on the same information. This is a primary architectural advantage of JSON-Relational Duality.
No export into a separate document store is necessary; doing so would reintroduce data duplication and synchronization problems that duality views are designed to avoid. Graph visualization is unrelated to document access, and although APEX applications can consume database data, APEX-specific PL/SQL packages are not the principal interface defining duality-view access.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - JSON Duality Views, Oracle AI Database API for MongoDB, SQL access, and unified relational/document development.
NEW QUESTION # 36
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