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| Section | Objectives |
|---|---|
| Working with JSON and Graph in Oracle AI Database | - 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 |
| Using Oracle Database Actions and Data Studio Tools | - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks - Describe Database Actions and core development tools |
| Building Low-Code Applications and Agentic AI | - Describe Oracle APEX as Oracle's low-code platform - Choose the appropriate Agent Factory capability for a no-code AI agent use case |
| Working with AI and Vector Foundations | - Describe AI, AGI, and machine learning foundations - Apply vector distance and indexing concepts to similarity search needs - Explain vectors, embeddings, and the Oracle VECTOR data type |
| Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | - 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 |
| Implementing Select AI and AI Vector Search in Autonomous AI Database | - 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 |
>> Free 1z0-1195-26 Exam Questions <<
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NEW QUESTION # 30
Which graph analytics capability is commonly used to rank important vertices based on their relationships?
Answer: D
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 # 31
How does Oracle APEX help developers build applications with Oracle Database data?
Answer: D
Explanation:
Oracle APEX is Oracle's low-code application development platform and is tightly integrated with Oracle Database. Oracle documentation describes APEX as providing browser-based enterprise application development with direct access to Oracle Database data. Developers configure pages, components, workflows, data sources, reports, forms, and business logic declaratively through App Builder rather than constructing every layer manually.
APEX explicitly embraces SQL. Developers can use SQL and PL/SQL to query and manipulate database- resident information while leveraging declarative components to minimize conventional coding effort.
Because the application engine operates close to the database, applications also inherit Oracle capabilities for transactions, security, concurrency, availability, analytics, and data management. Autonomous AI Database includes Oracle APEX capabilities and allows developers to create APEX workspaces and applications directly against database data.
APEX is therefore neither a disconnected application platform nor a command-line scripting framework. It is also distinct from Graph Studio and graph analytics capabilities used for operations such as community detection. The defining characteristics relevant to this question are its browser-based low-code development environment and native access to Oracle Database through SQL. The uploaded question bank confirms option B.
Study Guide reference: Building Low-Code Applications and Agentic AI - Oracle APEX, App Builder, browser-based development, and SQL integration.
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: B
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
What is the main difference between Autonomous AI Database Serverless and Dedicated deployment choices?
Answer: B
Explanation:
The fundamental distinction is that Serverless emphasizes simplicity and elasticity , whereas Dedicated provides isolated infrastructure and greater operational customization . This is the answer explicitly identified in the uploaded question set. Oracle documentation describes the Serverless model as ultra-simple and elastic: customers manage the Autonomous AI Database while Oracle manages the underlying Exadata infrastructure. Dedicated, by contrast, provides exclusive compute, storage, network, and database resources.
Oracle also characterizes Dedicated as a private-cloud-in-public-cloud deployment model with high levels of security isolation and governance. Dedicated environments can support customizable operational policies involving workload placement, update scheduling, availability, capacity usage, and other infrastructure-level concerns. Serverless removes much of that infrastructure planning and is therefore well suited to organizations prioritizing rapid provisioning and elastic consumption.
Neither deployment is restricted exclusively to JSON or relational workloads, and the distinction is not primarily about available developer SQL tools. Option A reverses the infrastructure characteristics: it is Dedicated-not Serverless-that supplies the isolated dedicated resource model.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - Serverless versus Dedicated deployment architecture.
NEW QUESTION # 34
A data engineer is creating a vector similarity query and wants to choose the distance metric correctly.
Which guidance should be applied?
Answer: B
Explanation:
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Oracle recommends using the distance metric associated with the embedding model that generated the vectors. Different metrics-such as cosine, Euclidean, dot product, Manhattan, or Hamming-measure similarity in different ways, and an embedding model is normally trained or intended to be evaluated with a particular metric. Oracle's AI Vector Search documentation states that it is generally best to match the query distance metric to the metric used to train the embedding model. Oracle also notes that a vector index should be created and searched with the appropriate distance function; using a different function can prevent index use and trigger exact search behavior. Table row count, maintenance schedules, and the presence of JSON attributes do not determine semantic vector geometry. Therefore, option C is the correct guidance. Oracle Docs
NEW QUESTION # 35
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