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1Z0-1127-25 Prüfungsfragen Prüfungsvorbereitungen 2026: Oracle Cloud Infrastructure 2025 Generative AI Professional - Zertifizierungsprüfung Oracle 1Z0-1127-25 in Deutsch Englisch pdf downloaden

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Oracle 1Z0-1127-25 Prüfungsplan:

Thema
Einzelheiten

Thema 1

Fundamentals of Large Language Models (LLMs): This section of the exam measures the skills of AI Engineers and Data Scientists in understanding the core principles of large language models. It covers LLM architectures, including transformer-based models, and explains how to design and use prompts effectively. The section also focuses on fine-tuning LLMs for specific tasks and introduces concepts related to code models, multi-modal capabilities, and language agents.

Thema 2

Using OCI Generative AI Service: This section evaluates the expertise of Cloud AI Specialists and Solution Architects in utilizing Oracle Cloud Infrastructure (OCI) Generative AI services. It includes understanding pre-trained foundational models for chat and embedding, creating dedicated AI clusters for fine-tuning and inference, and deploying model endpoints for real-time inference. The section also explores OCI's security architecture for generative AI and emphasizes responsible AI practices.

Thema 3

Using OCI Generative AI RAG Agents Service: This domain measures the skills of Conversational AI Developers and AI Application Architects in creating and managing RAG agents using OCI Generative AI services. It includes building knowledge bases, deploying agents as chatbots, and invoking deployed RAG agents for interactive use cases. The focus is on leveraging generative AI to create intelligent conversational systems.

Thema 4

Implement RAG Using OCI Generative AI Service: This section tests the knowledge of Knowledge Engineers and Database Specialists in implementing Retrieval-Augmented Generation (RAG) workflows using OCI Generative AI services. It covers integrating LangChain with Oracle Database 23ai, document processing techniques like chunking and embedding, storing indexed chunks in Oracle Database 23ai, performing similarity searches, and generating responses using OCI Generative AI.

>> 1Z0-1127-25 Fragenkatalog <<

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Oracle Cloud Infrastructure 2025 Generative AI Professional 1Z0-1127-25 Prüfungsfragen mit Lösungen (Q13-Q18):

13. Frage
Which LangChain component is responsible for generating the linguistic output in a chatbot system?

A. LangChain Application
B. Document Loaders
C. LLMs
D. Vector Stores

Antwort: C

Begründung:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, LLMs (Large Language Models) generate the linguistic output (text responses) in a chatbot system, leveraging their pre-trained capabilities. This makes Option D correct. Option A (Document Loaders) ingests data, not generates text. Option B (Vector Stores) manages embeddings for retrieval, not generation. Option C (LangChain Application) is too vague-it's the system, not a specific component. LLMs are the core text-producing engine.
OCI 2025 Generative AI documentation likely identifies LLMs as the generation component in LangChain.

14. Frage
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?

A. A Retrieval Augmented Generation (RAG) model that uses text as input and output
B. A language model that operates on a token-by-token output basis
C. A Large Language Model-based agent that focuses on generating textual responses
D. A diffusion model that specializes in producing complex outputs.

Antwort: D

Begründung:
Comprehensive and Detailed In-Depth Explanation=
The task requires bidirectional text-image capabilities: analyzing images to generate text and generating images from text. Diffusion models (e.g., Stable Diffusion) excel at complex generative tasks, including text-to-image and image-to-text with appropriate extensions, making Option A correct. Option B (LLM) is text-only. Option C (token-based LLM) lacks image handling. Option D (RAG) focuses on text retrieval, not image generation. Diffusion models meet both needs.
OCI 2025 Generative AI documentation likely discusses diffusion models under multimodal applications.

15. Frage
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

A. Summarization models
B. Generation models
C. Translation models
D. Embedding models

Antwort: C

Begründung:
Comprehensive and Detailed In-Depth Explanation=
OCI Generative AI typically offers pretrained models for summarization (A), generation (B), and embeddings (D), aligning with common generative tasks. Translation models (C) are less emphasized in generative AI services, often handled by specialized NLP platforms, making C the NOT category. While possible, translation isn't a core OCI generative focus based on standard offerings.
OCI 2025 Generative AI documentation likely lists model categories under pretrained options.

16. Frage
How are chains traditionally created in LangChain?

A. Using Python classes, such as LLMChain and others
B. Declaratively, with no coding required
C. Exclusively through third-party software integrations
D. By using machine learning algorithms

Antwort: A

Begründung:
Comprehensive and Detailed In-Depth Explanation=
Traditionally, LangChain chains (e.g., LLMChain) are created using Python classes that define sequences of operations, such as calling an LLM or processing data. This programmatic approach predates LCEL's declarative style, making Option C correct. Option A is vague and incorrect, as chains aren't ML algorithms themselves. Option B describes LCEL, not traditional methods. Option D is false, as third-party integrations aren't required. Python classes provide structured chain building.
OCI 2025 Generative AI documentation likely contrasts traditional chains with LCEL under LangChain sections.

17. Frage
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of-Thought, Least-to-Most, or Step-Back prompting technique:

A. "To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, we'll explore how they trap heat in the Earth's atmosphere."A. 1: Step-Back, 2: Chain-of-Thought, 3: Least-to-MostB. 1: Least-to-Most, 2: Chain-of-Thought, 3: Step-BackC. 1: Chain-of-Thought, 2: Step-Back, 3: Least-to-MostD. 1: Chain-of-Thought, 2: Least-to-Most, 3: Step-Back
B. "Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50."
C. "Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question."

Antwort: A

Begründung:
Comprehensive and Detailed In-Depth Explanation=
Prompt 1: Shows intermediate steps (3 × 4 = 12, then 12 ÷ 4 = 3 sets, $200 ÷ $50 = 4)-Chain-of-Thought.
Prompt 2: Steps back to a simpler problem before the full one-Step-Back.
Prompt 3: OCI 2025 Generative AI documentation likely defines these under prompting strategies.

18. Frage
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