Alle IT-Fachleute sind mit der Oracle 1z0-1157-26 Zertifizierungsprüfung vertraut und träumen davon, ein 1z0-1157-26 Zertifikat zu bekommen. Die Oracle 1z0-1157-26 Zertifizierungsprüfung ist die höchste Zertifizierung. Sie werden einen guten Beruf haben. Haben Sie es? Diese Prüfung ist schwer zu bestehen. Das macht doch nichts. Mit den Schulungsunterlagen zur Oracle 1z0-1157-26 Zertifizierungsprüfung von ZertFragen können Sie ganz einfach die Prüfung bestehen. Sie werden den Erfolg sicher erlangen.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agentic AI for Oracle AI Database | 25% | - Oracle AI Database agentic AI capabilities
|
| Topic 2: Introduction to AI Agents | 15% | - AI agent fundamentals
|
| Topic 3: LangChain for AI Agents | 5% | - LangChain fundamentals and agent construction
|
| Topic 4: OCI Enterprise AI Agents | 25% | - OCI Enterprise AI platform and agent services
|
| Topic 5: OpenAI Responses API and Agents SDK | 15% | - OpenAI agent stack
|
| Topic 6: Model Context Protocol (MCP) Fundamentals | 15% | - MCP architecture and integration
|
>> Oracle 1z0-1157-26 Prüfungsinformationen <<
Wenn Sie unsere Lernmaterialien zur Oracle 1z0-1157-26 Zertifizierungsprüfung benutzen wollen, werden sicher die Zeit und Wirtschaftskosten reduziert. Vorm Kauf unserer Oracle 1z0-1157-26 Prüfungsfrage können Sie kostenlos unsere Fragen herunterladen. Sie sind in der Form von PDF und Software. Wenn Sie die Softwareversion brauchen, bitte setzen Sie sich inVerbindung mit unserem Kundenservice.
14. Frage
What is the purpose of the Vector Stores API?
Antwort: D
Begründung:
The Vector Stores API provides infrastructure for ingesting content and retrieving the portions that are semantically relevant to a query. Files associated with a vector store can be chunked and prepared for vector- based retrieval, allowing applications and agents to locate content based on semantic similarity rather than only exact lexical matches .
OpenAI's official Vector Stores API supports searching a vector store with a natural-language query and returns relevant content chunks together with similarity scores. The API also supports attaching files to vector stores and configuring the chunking strategy used during ingestion. This architecture is foundational to retrieval-augmented generation and File Search workflows: source material is indexed, a user query retrieves semantically related chunks, and those chunks can then provide grounded context to a model.
Language translation is a generative-model task. Object Storage encryption is a cloud-storage security function, while video streaming is unrelated to the purpose of a vector store.
Accordingly, "Indexing and retrieving data by meaning" is the technically correct description and is also the answer specified by the uploaded question source.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - Vector Stores, semantic retrieval, chunking, File Search, similarity ranking, and RAG.
15. Frage
What integration problem does MCP address?
Antwort: C
Begründung:
MCP addresses the integration fragmentation created when multiple AI applications must independently connect to multiple external tools, services, and data sources. The uploaded course material characterizes this explicitly as the N × M custom-connector problem : without a common interoperability layer, every application-to-tool pairing can require a separate integration.
The official MCP architecture supports this framing by defining a standardized client-server protocol. MCP hosts establish clients that communicate with MCP servers, while servers expose reusable capabilities such as tools, resources, and prompts. A compatible AI application therefore consumes capabilities through the MCP protocol rather than requiring a completely bespoke protocol implementation for each downstream system.
MCP's tool-discovery mechanism further allows clients to obtain standardized names, descriptions, and schemas dynamically.
The problem is architectural interoperability, not GPU allocation, inference latency, or context-window limitations. Those issues require separate model, infrastructure, or prompt-management techniques. MCP instead standardizes the boundary between AI applications and external capabilities, reducing duplicated connector logic and enabling reusable integrations.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - interoperability, MCP client
/server architecture, tool discovery, and the N × M integration problem.
16. Frage
Which prompt addition is used for zero-shot Chain-of-Thought prompting?
Antwort: D
Begründung:
Zero-shot Chain-of-Thought prompting encourages a language model to decompose a problem into intermediate reasoning steps without supplying worked examples in the prompt. The classic prompting addition associated with this technique is "Let's think step by step." The technique is "zero-shot" because the user does not provide demonstrations showing how comparable problems should be solved; instead, a short natural-language instruction encourages stepwise decomposition.
OpenAI's published prompting guidance historically illustrates this exact technique, explaining that instructing a model to reason through a sequence of steps can improve performance on tasks requiring decomposition. The OpenAI Cookbook specifically gives "Let's think step by step" as an example of an instruction used to elicit a series of reasoning steps.
Setting temperature to zero affects sampling variability rather than creating Chain-of-Thought prompting.
Uploading structured data is unrelated to the reasoning technique, and disabling retrieval tools changes the model's information-access environment rather than prompting its problem-solving structure.
Accordingly, C is the intended and technically correct answer. The uploaded source also marks this exact phrase as correct.
Study Guide reference/topic: Introduction to AI Agents - prompting strategies, reasoning decomposition, zero-shot Chain-of-Thought, and agent reasoning patterns.
17. Frage
Which behavior is NOT a characteristic of modern LLM-based AI agents?
Antwort: A
Begründung:
Modern LLM-based agents are specifically designed to avoid requiring every possible execution path to be predetermined. The uploaded course material therefore correctly identifies "Requiring every execution path to be predefined" as the behavior that is NOT characteristic of an agent.
OpenAI defines agents as systems capable of independently accomplishing workflows using an LLM to manage workflow execution and make decisions. An agent can determine when a workflow is complete, correct its actions after receiving observations, and dynamically select tools according to the current state.
This differs fundamentally from conventional deterministic automation in which developers encode every branch and execution path beforehand.
Agents commonly pursue objectives across multiple reasoning-and-action cycles. They can invoke external APIs, databases, search systems, or other tools; inspect the resulting observations; and choose subsequent actions. A typical agent loop continues until an exit condition is reached rather than following one permanently fixed sequence.
Predetermined rules may still be used for safety, permissions, and guardrails, but the complete path toward the goal does not need to be pre-scripted.
Therefore, D is the correct answer.
Study Guide reference/topic: Introduction to AI Agents - autonomy, agent loops, observations, dynamic tool use, multi-step goal execution, and deterministic workflows.
18. Frage
Which OCI capability is required for serving fine-tuned or imported custom models?
Antwort: C
Begründung:
OCI Generative AI uses Dedicated AI Clusters to provide the isolated compute infrastructure required for fine- tuning and hosting custom model workloads. Oracle defines Dedicated AI Clusters as compute resources dedicated to a customer's models rather than shared with other tenancies. They can be created specifically for fine-tuning or for hosting model endpoints.
Oracle's current model onboarding workflow confirms the requirement. For imported models, the process includes importing the model, creating a hosting Dedicated AI Cluster , creating an endpoint, and then invoking the model. Fine-tuned models similarly require dedicated clusters for fine-tuning and subsequent hosting.
Shared On-Demand inference is appropriate for supported Oracle-hosted pretrained models, but it does not provide the dedicated isolated serving environment required by these custom model workflows. Object Storage can be an input location for model artifacts or training data, but it is storage rather than model-serving infrastructure. General-purpose Free Tier compute is likewise not the managed Generative AI capability Oracle specifies for custom-model serving.
Thus, B is correct and agrees with the uploaded course material.
Study Guide reference/topic: OCI Enterprise AI Agents - Dedicated AI Clusters, imported models, fine- tuned custom models, hosting clusters, and endpoints.
19. Frage
......
Möchten Sie die Oracle 1z0-1157-26 Zertifizierungsprüfung beim ersten Versuch bestehen? Auf der Webseite ZertFragen werden wir alle Ihrer Wünsche erfüllen und Ihnen versprechen, dass Sie die 1z0-1157-26 Zertifizierungsprüfung in begrenzter Zeit einmalig bestehen. Denn ZertFragen verfügt über die Fragenkataloge zur Oracle 1z0-1157-26 Zertifizierungsprüfung, die von erfahrenen IT-Experten entworfen werden und aus Fragen und Antworten kombiniert sind. Sie werden niemals bereuen, dass Sie ZertFragen gewählt haben. bearbeitet
1z0-1157-26 Deutsche Prüfungsfragen: https://www.zertfragen.com/1z0-1157-26_prufung.html