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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Working with JSON and Graph in Oracle AI Database | 20% | - Explain JSON and Oracle AI Database JSON capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case - Describe core graph concepts and graph analytic capabilities |
| Topic 2: 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 |
| Topic 3: 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 |
| Topic 4: 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 |
| Topic 5: 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 6: 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 |
>> Lab 1z0-1195-26 Questions <<
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NEW QUESTION # 37
What happens after a user asks a business question with Select AI?
Answer: B
Explanation:
Select AI automates the interaction among the user's natural-language prompt, database metadata, the configured large language model, generated SQL, and returned results. The uploaded assessment therefore correctly identifies option D. Oracle's Select AI documentation states that Autonomous AI Database processes the natural-language prompt, augments it with relevant metadata, interacts with an LLM, generates SQL, and can execute that SQL to return information.
Schema metadata is particularly important. Oracle can augment the prompt with table names, column names and data types, comments, annotations, constraints, and relationship information. This provides the LLM with database context and improves SQL generation while reducing hallucination risk.
Depending on the Select AI action, the service can display generated SQL, execute it, explain it, narrate query results in natural language, perform RAG against vector stores, or communicate directly with an LLM.
Select AI does not disable SQL; SQL remains fundamental to natural-language-to-SQL processing. Nor must users manually generate embeddings for ordinary NL2SQL requests. Embeddings become relevant to RAG
/vector workflows but are not a prerequisite for basic Select AI SQL generation.
Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database - prompt augmentation, LLM interaction, NL2SQL, and natural-language answers.
NEW QUESTION # 38
A team must deploy an Oracle Autonomous AI Database Serverless instance so that database access is available only inside a private OCI network.
Which access setting should they use?
Answer: B
Explanation:
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Private endpoint access only is the correct network setting when an Autonomous AI Database Serverless instance must be reachable only through a private OCI network. Oracle documents that this option assigns a private endpoint, private IP address, and hostname and allows traffic only from the specified VCN, including supported peered or connected private networks. Public access is blocked unless explicitly enabled through separate advanced configuration. "Secure access from allowed IPs and VCNs only" is an ACL-based public- endpoint model and can permit approved public addresses, so it does not satisfy the stricter private-only requirement. "Secure access from everywhere" is broader still. Therefore, option D precisely matches the requirement to keep database access inside the private OCI network.
NEW QUESTION # 39
Which set of tools is specifically highlighted as available from Database Actions?
Answer: D
Explanation:
The Database Actions environment specifically includes SQL, Data Modeler, REST, JSON, Oracle Machine Learning, and Oracle APEX , making option C correct. The uploaded assessment identifies the same tool set. Oracle's current Autonomous AI Database documentation describes Database Actions as a web- based interface for development, data tooling, administration, and monitoring. Its Development area includes SQL, Data Modeler, REST, JSON, Charts, Scheduling, Oracle Machine Learning, Spatial Studio, Graph Studio, and Oracle APEX.
SQL provides the browser-based worksheet for SQL and PL/SQL execution. Data Modeler supports database modeling and diagramming. REST provides tools for database REST services and APIs. JSON provides facilities for working with JSON collections and documents. Oracle Machine Learning exposes integrated ML development capabilities, while APEX launches Oracle's low-code application-development environment.
Oracle's Machine Learning documentation likewise identifies Database Actions as the entry point for Oracle Machine Learning.
The remaining choices largely describe OCI infrastructure or security-management services rather than Database Actions development tools. Compute, block storage, load balancing, IAM, billing, private endpoints, and Vault are managed elsewhere in OCI.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Database Actions Launchpad and Development tools.
NEW QUESTION # 40
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 # 41
Modern applications often need to work with relational data, JSON documents, graph relationships, and vector embeddings. What challenge does using a different specialized database for each need create?
Answer: B
Explanation:
Using a different point-solution database for each data model can create data silos , increasing integration, synchronization, governance, and operational complexity. The uploaded assessment identifies option D as correct. Oracle AI Database 26ai is explicitly positioned as a converged database platform supporting AI, graph, document, spatial, relational, and other application models within one database architecture.
The problem with separate specialized stores is that an application may need to duplicate relational records into a document database, copy embeddings into a vector database, and maintain relationships in a graph database. These copies must remain synchronized as source data changes. Security controls, backups, monitoring, patching, access policies, and application integrations may also differ across platforms.
Oracle's converged model instead allows different representations and workloads to operate on centrally governed data. Oracle specifically notes that the converged platform provides synergy among multiple data models and enables different types of information to be joined and manipulated together.
Therefore, separate databases do not automatically share security or eliminate transformation. Those are precisely the architectural burdens that convergence is intended to reduce.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - converged database strategy and elimination of data silos.
NEW QUESTION # 42
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