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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Working with AI and Vector Foundations | 15% | - 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 | 10% | - Choose the appropriate Agent Factory capability for a no-code AI agent use case - Describe Oracle APEX as Oracle's low-code platform |
| 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 |
| 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 - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy |
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質問 # 42
What describes the role of the Database Actions menu in Autonomous AI Database?
正解：D
解説：
Database Actions serves as the primary web-based launchpad for built-in Autonomous AI Database tooling.
Oracle describes Database Actions, also known as SQL Developer Web, as an integrated interface containing development, data, administration, monitoring, and download features. From this environment, users can access SQL Worksheet, Data Modeler, REST, JSON tools, Oracle Machine Learning, Graph Studio, Oracle APEX, Data Studio functions, database-user administration, Data Pump, Performance Hub, and other database services.
Its role is therefore broader than any single database technology. Graph Studio is one tool accessible through the environment rather than a replacement for SQL. Network isolation is configured through Autonomous AI Database networking facilities such as private endpoints, not by treating Database Actions itself as a network- access mechanism. Likewise, vector-index settings represent only one narrow area of database functionality and do not define the Database Actions environment. Oracle also documents Data Studio as a feature area accessed through Database Actions, reinforcing the concept of Database Actions as a common launchpad for multiple specialized workspaces. The uploaded question set marks the central built-in-tools location as the correct response.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Database Actions Launchpad, development tools, Data Studio, administration, and monitoring.
質問 # 43
How can developers access JSON Duality Views?
正解：D
解説：
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.
質問 # 44
What does Select AI enable in Autonomous AI Database?
正解：A
解説：
Select AI enables users to interact with Autonomous AI Database by expressing requests in natural language rather than manually constructing SQL. Oracle documents that Select AI uses generative AI and large language models to convert natural-language input into Oracle SQL. Depending on the requested action, the generated SQL can be displayed, explained, executed, or its results can be transformed into a natural-language response. This makes database information accessible to users who understand the business question but may not know SQL syntax or the underlying schema.
Internally, Select AI can augment a prompt with schema metadata, interact with the configured LLM, generate SQL, run the query, and optionally narrate the resulting data. It also extends beyond NL2SQL into Retrieval-Augmented Generation, conversations, and other generative-AI capabilities. It does not replace relational SQL with graph pattern matching, manage encryption-key rotation, or provision Autonomous AI Database infrastructure. Those are unrelated database administration or graph functions. The supplied question bank explicitly marks the natural-language data-query capability as correct.
Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database - Select AI, natural-language interaction, NL2SQL, and LLM integration.
質問 # 45
A manufacturer needs graph analysis that reflects inserts and updates from operational tables immediately.
How does a Property Graph View support this requirement?
正解：A
解説：
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.
質問 # 46
When comparing a query vector with stored vectors, what does a smaller vector distance indicate?
正解：C
解説：
Vector distance quantifies how far apart two vector representations are according to a specified mathematical distance metric. In semantic search, embeddings place semantically related content near one another in a multidimensional vector space. Consequently, when a distance-oriented metric such as cosine distance or Euclidean distance produces a smaller value, the vectors are considered closer and therefore generally more similar according to the embedding model. Oracle's cosine-distance documentation explains the inverse relationship between cosine similarity and cosine distance: increasingly similar vector directions produce smaller cosine-distance values.
For example, cosine distance is calculated as 1 - cosine similarity. Identical or highly aligned semantic representations therefore approach a distance of zero, while increasingly different vectors produce larger distances. This principle is why Oracle SQL similarity searches commonly order rows by VECTOR_DISTANCE(...) in ascending order and fetch the first N rows: the rows with the smallest distances are the nearest semantic matches. Distance has no relationship to database maintenance windows, and a smaller value does not prove that different embedding models were used. Vector search also remains available through SQL and does not require GraphQL.
Study Guide reference: Working with AI and Vector Foundations - vector embeddings, distance metrics, semantic similarity, and top-K ranking.
質問 # 47
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