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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 1Z0-1127-25 Prüfungsplan:

Thema
Einzelheiten

Thema 1

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 2

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 3

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 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.

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1Z0-1127-25 Übungstest: Oracle Cloud Infrastructure 2025 Generative AI Professional & 1Z0-1127-25 Braindumps Prüfung

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Oracle Cloud Infrastructure 2025 Generative AI Professional 1Z0-1127-25 Prüfungsfragen mit Lösungen (Q76-Q81):

76. Frage
How does a presence penalty function in language model generation?

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

Antwort: A

Begründung:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use, to discourage repetition. This makes Option D correct. Option A (equal penalties) ignores prior appearance. Option B is the opposite-penalizing unused tokens isn't the intent. Option C (more than twice) adds an arbitrary threshold not typically used. Presence penalty enhances output variety.OCI 2025 Generative AI documentation likely details presence penalty under generation control parameters.

77. Frage
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

A. When the LLM does not perform well on a task and the data for prompt engineering is too large
B. When the LLM already understands the topics necessary for text generation
C. When you want to optimize the model without any instructions
D. When the LLM requires access to the latest data for generating outputs

Antwort: A

Begründung:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning is suitable when an LLM underperforms on a specific task and prompt engineering alone isn't feasible due to large, task-specific data that can't be efficiently included in prompts. This adjusts the model's weights, making Option B correct. Option A suggests no customization is needed. Option C favors RAG for latest data, not fine-tuning. Option D is vague-fine-tuning requires data and goals, not just optimization without direction. Fine-tuning excels with substantial task-specific data.
OCI 2025 Generative AI documentation likely outlines fine-tuning use cases under customization strategies.

78. Frage
How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?

A. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.
B. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.
C. Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance.
D. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.

Antwort: C

Begründung:
Comprehensive and Detailed In-Depth Explanation=
In RAG, "Groundedness" assesses whether the response is factually correct and supported by retrieved data, while "Answer Relevance" evaluates how well the response addresses the user's query. Option A captures this distinction accurately. Option B is off-groundedness isn't just contextual alignment, and relevance isn't about syntax. Option C swaps the definitions. Option D misaligns-groundedness isn't solely data integrity, and relevance isn't lexical diversity. This distinction ensures RAG outputs are both true and pertinent.
OCI 2025 Generative AI documentation likely defines these under RAG evaluation metrics.

79. Frage
What do prompt templates use for templating in language model applications?

A. Python's class and object structures
B. Python's str.format syntax
C. Python's list comprehension syntax
D. Python's lambda functions

Antwort: B

Begründung:
Comprehensive and Detailed In-Depth Explanation=
Prompt templates in LLM applications (e.g., LangChain) typically use Python's str.format() syntax to insert variables into predefined string patterns (e.g., "Hello, {name}!"). This makes Option B correct. Option A (list comprehension) is for list operations, not templating. Option C (lambda functions) defines functions, not templates. Option D (classes/objects) is overkill-templates are simpler constructs. str.format() ensures flexibility and readability.
OCI 2025 Generative AI documentation likely mentions str.format() under prompt template design.

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

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

Antwort: A

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.

81. Frage
......

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