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1Z0-1127-25 Examsfragen & 1Z0-1127-25 Online Tests

Nun gibt es viele IT-Profis in der ganzen Welt und die Konkurrenz der IT-Branche ist sehr hart. So viele IT-Profis entscheiden sich dafür, an der IT-Zertifizierungsprüfung teilzunehmen, um ihre Position in der IT-Branche zu verstärken. Die 1Z0-1127-25 Prüfung ist eine sehr wichtige Oracle-Zertifizierungsprüfung. Aber wenn Sie eine Oracle-Zertifizierung erhalten wollen, müssen Sie die Prüfung bestehen.

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

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 4

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.

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Oracle Cloud Infrastructure 2025 Generative AI Professional 1Z0-1127-25 Prüfungsfragen mit Lösungen (Q66-Q71):

66. Frage
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

A. Shared among multiple customers for efficiency
B. Stored in Object Storage encrypted by default
C. Stored in an unencrypted form in Object Storage
D. Stored in Key Management service

Antwort: B

Begründung:
Comprehensive and Detailed In-Depth Explanation=
In OCI, fine-tuned models are stored in Object Storage, encrypted by default, ensuring privacy and security per cloud best practices-Option B is correct. Option A (shared) violates privacy. Option C (unencrypted) contradicts security standards. Option D (Key Management) stores keys, not models. Encryption protects customer data.
OCI 2025 Generative AI documentation likely details storage security under fine-tuning workflows.

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

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

Antwort: A

Begründung:
Comprehensive and Detailed In-Depth Explanation=
Temperature adjusts the softmax distribution in decoding. Increasing it (e.g., to 2.0) flattens the curve, giving lower-probability words a better chance, thus increasing diversity-Option C is correct. Option A exaggerates-top words still have impact, just less dominance. Option B is backwards-decreasing temperature sharpens, not broadens. Option D is false-temperature directly alters distribution, not speed. This controls output creativity.
OCI 2025 Generative AI documentation likely reiterates temperature effects under decoding parameters.

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

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

Antwort: B

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.

69. 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. Assigns a penalty to tokens that have already appeared in the preceding text
C. Determines the maximum number of tokens the model can generate per response
D. Specifies a string that tells the model to stop generating more content

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.

70. Frage
What is the characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

A. It updates all the weights of the model uniformly.
B. It selectively updates only a fraction of weights to reduce computational load and avoid overfitting.
C. It increases the training time as compared to Vanilla fine-tuning.
D. It selectively updates only a fraction of weights to reduce the number of parameters.

Antwort: B

Begründung:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning (a Parameter-Efficient Fine-Tuning method) updates a small subset of the model's weights, reducing computational cost and mitigating overfitting compared to Vanilla fine-tuning, which updates all weights. This makes Option C correct. Option A describes Vanilla fine-tuning, not T-Few. Option B is incomplete, as it omits the overfitting benefit. Option D is false, as T-Few typically reduces training time due to fewer updates. T-Few balances efficiency and performance.
OCI 2025 Generative AI documentation likely describes T-Few under fine-tuningoptions.

71. Frage
......

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