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1Z0-1127-25 Deutsche Prüfungsfragen & 1Z0-1127-25 Prüfungsübungen

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Oracle 1Z0-1127-25 Prüfungsplan:

Thema
Einzelheiten

Thema 1

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.

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

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.

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

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Oracle Cloud Infrastructure 2025 Generative AI Professional 1Z0-1127-25 Prüfungsfragen mit Lösungen (Q31-Q36):

31. Frage
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

A. Controls the randomness of the model's output, affecting its creativity
B. Specifies a string that tells the model to stop generating more content
C. Determines the maximum number of tokens the model can generate per response
D. Assigns a penalty to tokens that have already appeared in the preceding text

Antwort: A

Begründung:
Comprehensive and Detailed In-Depth Explanation=
The "temperature" parameter adjusts the randomness of an LLM's output by scaling the softmax distribution-low values (e.g., 0.7) make it more deterministic, high values (e.g., 1.5) increase creativity-Option A is correct. Option B (stop string) is the stop sequence. Option C (penalty) relates to presence/frequency penalties. Option D (max tokens) is a separate parameter. Temperature shapes output style.
OCI 2025 Generative AI documentation likely defines temperature under generation parameters.

32. Frage
How does the structure of vector databases differ from traditional relational databases?

A. It is not optimized for high-dimensional spaces.
B. It uses simple row-based data storage.
C. It is based on distances and similarities in a vector space.
D. A vector database stores data in a linear or tabular format.

Antwort: C

Begründung:
Comprehensive and Detailed In-Depth Explanation=
Vector databases store data as high-dimensional vectors, optimized for similarity searches (e.g., cosine distance), unlike relational databases' tabular, row-column structure. This makes Option C correct. Option A and D describe relational databases. Option B is false-vector databases excel in high-dimensional spaces. Vector databases support semantic queries critical for LLMs.
OCI 2025 Generative AI documentation likely contrasts these under data storage options.

33. Frage
How does a presence penalty function in language model generation when using OCI Generative AI service?

A. It penalizes all tokens equally, regardless of how often they have appeared.
B. It applies a penalty only if the token has appeared more than twice.
C. It penalizes a token each time it appears after the first occurrence.
D. It only penalizes tokens that have never appeared in the text before.

Antwort: C

Begründung:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty in LLMs (including OCI's service) reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use. This discourages repetition, making Option D correct. Option A is false, as penalties depend on prior appearance, not uniform application. Option B is the opposite-penalizing unused tokens isn't the goal. Option C is incorrect, as the penalty isn't threshold-based (e.g., more than twice) but applied per reoccurrence. This enhances output diversity.
OCI 2025 Generative AI documentation likely details presence penalty under generation parameters.

34. Frage
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alter their responses?

A. It limits their ability to understand and generate natural language.
B. It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.
C. It enables them to bypass the need for pretraining on large text corpora.
D. It transforms their architecture from a neural network to a traditional database system.

Antwort: B

Begründung:
Comprehensive and Detailed In-Depth Explanation=
RAG integrates vector databases to retrieve real-time external data, augmenting the LLM's pretrained knowledge with current, specific information, shifting response generation to a hybrid approach-Option B is correct. Option A is false-architecture remains neural; only data sourcing changes. Option C is incorrect-pretraining is still required; RAG enhances it. Option D is wrong-RAG improves, not limits, generation. This shift enables more accurate, up-to-date responses.
OCI 2025 Generative AI documentation likely details RAG's impact under responsegeneration enhancements.

35. Frage
Which is NOT a built-in memory type in LangChain?

A. ConversationSummaryMemory
B. ConversationImageMemory
C. ConversationBufferMemory
D. ConversationTokenBufferMemory

Antwort: B

Begründung:
Comprehensive and Detailed In-Depth Explanation=
LangChain includes built-in memory types like ConversationBufferMemory (stores full history), ConversationSummaryMemory (summarizes history), and ConversationTokenBufferMemory (limits by token count)-Options B, C, and D are valid. ConversationImageMemory (A) isn't a standard type-image handling typically requires custom or multimodal extensions, not a built-in memory class-making A correct as NOT included.
OCI 2025 Generative AI documentation likely lists memory types under LangChain memory management.

36. Frage
......

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Hannah Carter Hannah Carter

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