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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Oracle Machine Learning and AI Integration | ~15% | - Oracle Data Studio and visualization - In-database machine learning algorithms - AI agents and LLM integration |
| Topic 2: Security, Resilience, and Cloud Integration | ~21% | - High availability, backup, and disaster recovery - Cloud-native database services and deployment strategies - Database security architectures and data protection |
| Topic 3: MySQL HeatWave and NoSQL Services | ~11% | - MySQL HeatWave architecture and analytics - Oracle NoSQL Database features and use cases |
| Topic 4: Data Management and Oracle Data Platform Overview | ~11% | - Oracle Data Strategy and multi-cloud deployment models - Modern data platform value and architecture - Data management concepts and data types |
| Topic 5: Oracle Database Services — Exadata, DBCS, and Engineered Systems | ~11% | - Database Cloud Service (DBCS) characteristics - Exadata architecture and features |
| Topic 6: Autonomous AI Database and Tools | ~16% | - Built-in management and query tools - Core features of Autonomous AI Database - Shared vs dedicated infrastructure |
| Topic 7: Converged Database — Multi-Model and AI Capabilities | ~15% | - Select AI and natural language querying - Oracle AI Vector Search concepts - JSON, Graph, Spatial, and key-value data support |
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Additionally, students can take multiple 1z0-1195-26 exam questions, helping them to check and improve their performance. Three formats are prepared in such a way that by using them, candidates will feel confident and crack the Oracle AI Database Foundations Associate (1z0-1195-26) actual exam. These three formats suit different preparation styles of 1z0-1195-26 test takers.
NEW QUESTION # 10
A support portal must search product manuals semantically while also filtering results by product line and support level stored in relational columns. How does Oracle AI Vector Search support this requirement?
Answer: A
Explanation:
Oracle AI Vector Search supports semantic similarity search together with conventional relational predicates in SQL , making option C correct. The uploaded source explicitly identifies this integrated approach. Oracle AI Database 26ai provides the native VECTOR data type so embeddings can reside directly alongside relational business attributes. Oracle states that AI-powered vector similarity searches can be combined with business-data searches using SQL and the full capabilities of the converged database.
For the support portal, each manual or document chunk can have an embedding while relational columns identify its product line, entitlement level, version, or support tier. A SQL statement can restrict rows using predicates such as product line and support level while ordering eligible records by vector distance from the user's query embedding.
This approach is superior to retrieving a broad semantic result set and filtering it later in application code.
External post-filtering can waste processing and may remove highly ranked records without correctly replacing them with the next eligible matches.
Document APIs and property graphs are also not prerequisites. Oracle's converged architecture permits relational and vector criteria to operate together directly.
Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database - hybrid business filtering, vector similarity ranking, and integrated SQL.
NEW QUESTION # 11
What happens after a user asks a business question with Select AI?
Answer: C
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 # 12
A company wants a GenAI assistant that answers policy questions by grounding responses in its internal documents.
Which use of AI Vector Search best supports this design?
Answer: A
Explanation:
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Retrieval-Augmented Generation grounds an LLM by retrieving relevant enterprise content before generation.
Oracle Select AI with RAG uses AI Vector Search and semantic similarity to locate the top matching document chunks from a vector store, then supplies those retrieved texts together with the user's question to the LLM. This gives the model current, organization-specific context and reduces hallucination risk. Keyword- only retrieval can miss semantically related passages that use different wording, while graph edge identifiers are not a substitute for document content. Returning unfiltered database patches also does not provide targeted grounding. Therefore, the correct design is to retrieve semantically similar chunks and use them as context for generation. This matches Oracle's documented Select AI RAG workflow and the AI/vector foundations objectives. Oracle Docs
NEW QUESTION # 13
How can developers access JSON Duality Views?
Answer: D
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 # 14
How does Oracle APEX help developers build applications with Oracle Database data?
Answer: C
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 # 15
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