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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Using Oracle Database Actions and Data Studio Tools | 15% | - Describe Database Actions and core development tools - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks |
| Topic 3: 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 |
| Topic 4: 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 |
| Topic 5: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - 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 |
| Topic 6: Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - Apply AI Vector Search to combined semantic and business-data search scenarios - Describe Select AI in Autonomous AI Database - Determine how AI Vector Search supports GenAI pipelines and RAG |
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NEW QUESTION # 14
A data analyst wants to discover data, load it into the platform, and prepare it for later use without leaving the built-in tools. Which workspace fits this task?
Answer: D
Explanation:
Data Studio is the correct built-in workspace because its purpose directly covers data discovery, loading, preparation, transformation, analysis, cataloging, sharing, and related self-service workflows. The uploaded source designates Data Studio as the correct answer. Oracle's current Autonomous AI Database documentation states that Data Studio can be used to load, discover, catalog, transform, analyze, share, enrich, and automate data workflows through a web-based interface.
Data Studio is available through Database Actions and contains specialized tools including Data Load , Catalog , Data Transforms , Data Analysis , and Data Insights . Data Load can ingest or link data from local files, remote databases, cloud storage, and live feeds, while Catalog supports searching, discovering, inspecting, and managing available data assets.
OCI Vault addresses secrets and encryption-key management rather than data preparation.
VECTOR_DISTANCE is an individual SQL function used for vector comparisons, not an integrated analyst workspace. A Property Graph View defines a graph representation over data but likewise does not provide general discovery and loading functionality.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Data Studio, Data Load, Catalog, Data Transforms, and self-service data preparation.
NEW QUESTION # 15
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: D
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 # 16
A recruiting application must find the top job postings that semantically match a candidate's resume, but only in the candidate's city. How should the application meet this requirement?
Answer: D
Explanation:
The correct Oracle AI Vector Search design is a single SQL query combining the relational city restriction with vector-distance ranking . The uploaded source explicitly identifies option C. Oracle AI Database's native VECTOR type enables vector similarity searches without moving business information to a separate vector database. Oracle specifically states that vector searches can be combined with sophisticated business- data searches using SQL and the capabilities of its converged database.
The recruiting system can store an embedding of each job description alongside ordinary relational attributes such as city, employer, salary, employment type, and status. The candidate's resume or search request is converted into a query vector. SQL can then apply a predicate such as city = :candidate_city, calculate vector distance against qualifying job embeddings, order the result by that distance, and return the top matches.
Exporting postings to another vector platform unnecessarily introduces data movement and loses the direct integration with current relational attributes. Graph edge labels are intended for relationship modeling, not semantic document representation. "Early maintenance" is unrelated to vector ranking.
The key Oracle AI Database principle being tested is semantic vector retrieval and conventional relational business filtering within one SQL operation .
Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database - vector-distance ordering, relational predicates, top-K retrieval, and converged SQL.
NEW QUESTION # 17
Which task is a common use of graph analytics?
Answer: D
Explanation:
Determining communities or connected clusters in a network is a canonical graph-analytics use case. The uploaded source identifies option D as the correct response. Oracle Property Graph documentation explicitly lists finding communities , influencers, recommendations, graph traversal, pattern matching, and path finding among typical graph-analysis operations. Oracle AI Database 26ai documentation also identifies Community Detection as a supported graph-analysis algorithm.
Community detection examines topology to identify groups of vertices that are more strongly connected with one another than with the remainder of the network. Examples include identifying customer communities in social networks, coordinated groups in fraud investigations, clusters of interconnected devices in telecommunications, or related entities in knowledge graphs.
The other choices deliberately discard the characteristic that makes graph analytics useful: relationships.
Listing unrelated records and sorting a scalar column are standard tabular operations. Storing relationships as isolated text values prevents the database from traversing and analyzing those relationships as graph edges.
Graph analytics becomes valuable when the question concerns connectivity, paths, neighborhoods, influence, centrality, clusters, or structural patterns rather than merely individual records and attributes.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - graph analytics, community detection, connectivity, and network analysis.
NEW QUESTION # 18
A team already uses Database Actions and now needs the part of the environment focused on discovery, load, integration, and cataloging. Which workspace should they open?
Answer: D
Explanation:
Data Studio is the Database Actions workspace specifically designed for data-oriented activities such as loading, discovering, cataloging, transforming, integrating, analyzing, and preparing data. The uploaded source marks Data Studio as the correct option. Oracle's current Autonomous AI Database documentation states that Data Studio enables users to load, discover, catalog, transform, analyze, share, enrich, and automate data workflows through a web-based interface.
Data Studio contains purpose-built tools including Data Load , Catalog , Data Transforms , Data Analysis
, and Data Insights . Catalog provides a central mechanism for browsing, searching, discovering, inspecting, and acting on local or connected data assets, while Data Transforms supports graphical data integration and transformation workflows.
The alternatives perform different functions. Database Users handles user administration. Graph Studio focuses on graph modeling and analytics. SQL Worksheet provides interactive execution of SQL and PL/SQL rather than an integrated discovery, cataloging, and loading environment.
Therefore, when the question combines the terms discovery, load, integration, and cataloging , Data Studio is the definitive Oracle workspace.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Data Studio, Data Load, Catalog, and Data Transforms.
NEW QUESTION # 19
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