1z0-1157-26 Trainingsunterlagen, 1z0-1157-26 Tests

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Oracle 1z0-1157-26 Exam Syllabus Topics:

Section	Objectives
Topic 1: LangChain for AI Agents	- LangChain fundamentals 1. Tools, tool schemas, and tool execution 2. LangChain and LangChain Expression Language 3. Agent invocation and orchestration flow 4. Building agents with LangChain
Topic 2: OpenAI Responses API and Agents SDK	- OpenAI agent development 1. Responses API 2. OpenAI Agents SDK 3. Function calling and tools 4. Guardrails and tracing 5. Multi-agent handoffs
Topic 3: Agentic AI for Oracle AI Database	- Oracle AI Database agentic AI capabilities 1. Oracle AI Vector Search 2. Oracle AI Database Private Agent Factory 3. Select AI 4. Vector data types, embeddings, and similarity search 5. Document chunking, embedding generation, and retrieval 6. Grounding agent responses with enterprise data 7. Oracle Autonomous AI Database MCP Server
Topic 4: Introduction to MCP	- Model Context Protocol fundamentals 1. MCP clients and servers 2. MCP concepts and architecture 3. Tool discovery and interoperability
Topic 5: Introduction to AI Agents	- AI agent fundamentals and architecture 1. Core agent components: LLMs, tools, and orchestration loops 2. Agent reasoning patterns including Chain-of-Thought and ReAct 3. Safety, guardrails, and responsible agentic workflows 4. AI agents, traditional chatbots, and rule-based systems - Agent development concepts 1. OpenAI Agents SDK guardrails 2. Function calling and tool use 3. Multi-agent design patterns and handoffs
Topic 6: OCI Enterprise AI Agents	- OCI Enterprise AI platform 1. Agent development, orchestration, and execution 2. Responses API, tools, memory, and vector stores 3. OCI Enterprise AI Agents service 4. Deployment and scaling 5. Building and running AI agents

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Oracle 1z0-1157-26 Fragen und Antworten, Agentic AI Foundations Associate Prüfungsfragen

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Oracle Agentic AI Foundations Associate 1z0-1157-26 Prüfungsfragen mit Lösungen (Q38-Q43):

38. Frage
Which prompt addition is used for zero-shot Chain-of-Thought prompting?

A. Upload a structured CSV first.
B. Disable all external retrieval tools.
C. Set temperature equal to zero.
D. Let's think step by step.

Antwort: D

39. Frage
Which authentication approach should be used for production-grade access to OCI Enterprise AI services?

A. Browser session cookies stored with application code
B. Anonymous access to tenancy-level resources
C. Hardcoded credentials checked into source repositories
D. OCI IAM authentication with signed requests and IAM policies

Antwort: D

Begründung:
For production OCI Enterprise AI workloads, Oracle recommends OCI IAM-based authentication rather than long-lived development credentials. The uploaded source identifies OCI IAM authentication with signed requests and IAM policies as the correct production architecture.
Oracle's OCI Responses API authentication documentation distinguishes service API keys used for testing and early development from IAM authentication intended for production and OCI-managed environments.
IAM-based authentication uses OCI identity principals and request-signing mechanisms and allows authorization to be controlled through centralized IAM policies. Oracle specifically recommends IAM when applications execute in services such as OCI Functions or Oracle Kubernetes Engine, when long-lived API keys should be avoided, or when fine-grained centralized access control is required.
IAM policies also implement least privilege by defining exactly which users, groups, resource principals, or workloads can access individual Generative AI resource types.
Browser cookies, anonymous tenancy access, and credentials committed into source repositories violate standard enterprise security practices and significantly increase credential-exposure risk.
Therefore, A is the only production-grade authentication approach among the choices.
Study Guide reference/topic: OCI Enterprise AI Agents - OCI IAM, signed requests, policies, principals, least privilege, and production authentication.

40. Frage
In the given LangChain chain, what is the role of StrOutputParser()? chain = prompt

A. It sends the prompt to the model API.
B. It extracts plain text from the model response.
C. StrOutputParser()
D. It stores tool execution history.

Antwort: B

Begründung:
StrOutputParser is a LangChain output parser used to convert a language-model response into a standard Python string. In an LCEL pipeline such as prompt | model | StrOutputParser() , the prompt prepares the model input, the model generates an AIMessage or equivalent model output, and StrOutputParser extracts the textual content so downstream application code receives plain text.
LangChain's official documentation demonstrates this exact pattern by composing a prompt, chat model, and StrOutputParser() into a chain and then invoking the resulting runnable. The parser therefore operates after model inference ; it does not send the request to the model and does not maintain agent or tool history.
The uploaded source presents the chain fragment across separate lines and explicitly identifies "It extracts plain text from the model response" as the correct response.
This output-parsing stage is especially useful because it isolates application code from provider-specific response-object structures and provides a predictable string output from a LangChain runnable.
Study Guide reference/topic: LangChain for AI Agents - LCEL chains, Runnable composition, model output handling, and StrOutputParser.

41. Frage
What is the strategic theme behind agentic AI capabilities in Oracle AI Database?

A. Selling vector products without database integration
B. Bringing AI capabilities natively into the database.
C. Removing SQL functionality in favor of REST APIs
D. Replacing the database with a vector-only system

Antwort: B

Begründung:
Oracle's strategic direction is to integrate AI capabilities directly into Oracle AI Database , allowing conventional relational data, vector embeddings, semantic retrieval, natural-language interfaces, and autonomous agent functionality to operate within the database platform rather than requiring a separate AI- only data tier.
Oracle AI Vector Search illustrates this strategy. The database provides a native VECTOR data type, vector- distance functions, vector indexes, and semantic similarity search alongside traditional relational data and SQL operations. This allows embeddings and enterprise business records to remain together under existing transactional, security, and governance controls.
Select AI reinforces the same architecture. Oracle documents that Select AI runs natively inside Autonomous AI Database and Oracle AI Database, while Select AI Agent provides autonomous reasoning, tools, reflection, memory, RAG, NL2SQL, PL/SQL integration, and REST interactions within the database-oriented agent framework.
Oracle is therefore extending SQL and database functionality rather than eliminating it. Nor is Oracle replacing the relational database with a vector-only system. The strategic objective is convergence: enterprise data plus native AI capabilities in one governed database environment. Option B is therefore correct.
Study Guide reference/topic: Agentic AI for Oracle AI Database - native AI integration, Select AI, Select AI Agent, AI Vector Search, and converged data architecture.

42. Frage
What is the high-level workflow for Oracle AI Vector Search?

A. Generate embeddings - > Store vectors - > Create indexes - > Search and query - > Feed into LLM
B. Create indexes - > Search and query - > Feed into LLM - > Store vectors
C. Generate embeddings - > Store vectors - > Search and query - > Feed into LLM
D. Create indexes - > Generate embeddings - > Store vectors - > Feed into LLM - > Search and query

Antwort: A

Begründung:
Official Oracle documentation supports C. Oracle describes the typical AI Vector Search workflow in five stages: generate vector embeddings from unstructured content; store those embeddings with the associated data; create vector indexes; perform semantic/vector searches using SQL; and then use the retrieved content in an LLM prompt for RAG inference.
Therefore, the technically complete sequence is:
Generate embeddings # Store vectors # Create indexes # Search and query # Feed into LLM.
This ordering reflects the operational dependency between the stages. Embeddings must exist before they can be persisted. Vector indexes are created over stored vector columns to accelerate similarity retrieval. Search then retrieves semantically relevant content, which can subsequently be incorporated into an LLM prompt for retrieval-augmented generation.
There is an important discrepancy in the uploaded question file: it marks option A as the correct answer even though A omits the documented Create indexes stage. Because the request requires verification against official Agentic AI/Oracle material, the verified answer is C , not the supplied key's A.
Study Guide reference/topic: Agentic AI for Oracle AI Database - AI Vector Search workflow, embeddings, VECTOR storage, vector indexes, similarity search, and RAG.

43. Frage
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