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Die Oracle 1Z0-1127-25 (Oracle Cloud Infrastructure 2025 Generative AI Professional) Zertifizierungsprüfung ist eine Prüfung, die Fachkenntnisse und Fertigkeiten eines Menschen testet. Wenn Sie einen Job in der IT-Branche suchen, werden Sie viele Personalmanager nach den relevanten Oracle 1Z0-1127-25 IT-Zertifikaten fragen. Wenn Sie das Oracle 1Z0-1127-25 (Oracle Cloud Infrastructure 2025 Generative AI Professional) Zertifikat haben, können Sie sicher Ihre Wettbewerbsfähigkeit verstärken.

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 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 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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Oracle Cloud Infrastructure 2025 Generative AI Professional 1Z0-1127-25 Prüfungsfragen mit Lösungen (Q47-Q52):

47. Frage
What is the function of the Generator in a text generation system?

A. To collect user queries and convert them into database search terms
B. To store the generated responses for future use
C. To rank the information based on its relevance to the user's query
D. To generate human-like text using the information retrieved and ranked, along with the user's original query

Antwort: D

Begründung:
Comprehensive and Detailed In-Depth Explanation=
In a text generation system (e.g., with RAG), the Generator is the component (typically an LLM) that produces coherent, human-like text based on the user's query and any retrieved information (if applicable). It synthesizes the final output, making Option C correct. Option A describes a Retriever's role. Option B pertains to a Ranker. Option D is unrelated, as storage isn't the Generator's function but a separate system task. The Generator's role is critical in transforming inputs into natural language responses.
OCI 2025 Generative AI documentation likely defines the Generator under RAG or text generation workflows.

48. Frage
How are chains traditionally created in LangChain?

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

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.

49. Frage
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

A. Increasing temperature removes the impact of the most likely word.
B. Temperature has no effect on the probability distribution; it only changes the speed of decoding.
C. Increasing temperature flattens the distribution, allowing for more varied word choices.
D. Decreasing temperature broadens the distribution, making less likely words more probable.

Antwort: C

Begründung:
Comprehensive and Detailed In-Depth Explanation=
Temperature controls the randomness of an LLM's output by adjusting the softmax probability distribution over the vocabulary. Increasing temperature (e.g., to 1.5) flattens the distribution, reducing the dominance of high-probability words and allowing more diverse, less predictable choices, making Option C correct. Option A is misleading-higher temperature doesn't remove the top word's impact entirely but reduces its relative likelihood. Option B is incorrect, as decreasing temperature sharpens the distribution, favoring likely words, not broadening it. Option D is false, as temperature directly affects the distribution, not just decoding speed. This mechanism is key for balancing creativity and coherence.
OCI 2025 Generative AI documentation likely explains temperature under decoding or output control parameters.

50. Frage
Which statement describes the difference between "Top k" and "Top p" in selecting the next token in the OCI Generative AI Generation models?

A. "Top k" selects the next token based on its position in the list of probable tokens, whereas "Top p" selects based on the cumulative probability of the top tokens.
B. "Top k" and "Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.
C. "Top k" considers the sum of probabilities of the top tokens, whereas "Top p" selects from the "Top k" tokens sorted by probability.
D. "Top k" and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.

Antwort: A

Begründung:
Comprehensive and Detailed In-Depth Explanation=
"Top k" sampling selects from the k most probable tokens, based on their ranked position, while "Top p" (nucleus sampling) selects from tokens whose cumulative probability exceeds p, focusing on a dynamic probability mass-Option B is correct. Option A is false-they differ in selection, not penalties. Option C reverses definitions. Option D (frequency) is incorrect-both use probability, not frequency. This distinction affects diversity.
OCI 2025 Generative AI documentation likely contrasts Top k and Top p under sampling methods.

51. Frage
In which scenario is soft prompting appropriate compared to other training styles?

A. When the model needs to be adapted to perform well in a domain on which it was not originally trained
B. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training
C. When the model requires continued pretraining on unlabeled data
D. When there is a significant amount of labeled, task-specific data available

Antwort: B

Begründung:
Comprehensive and Detailed In-Depth Explanation=
Soft prompting adds trainable parameters (soft prompts) to adapt an LLM without retraining its core weights, ideal for low-resource customization without task-specific data. This makes Option C correct. Option A suits fine-tuning. Option B may require more than soft prompting (e.g., domain fine-tuning). Option D describes pretraining, not soft prompting. Soft prompting is efficient for specific adaptations.
OCI 2025 Generative AI documentation likely discusses soft prompting under PEFT methods.

52. Frage
......

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Matt Young Matt Young

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