100% Garantie 1Z0-1127-25 Prüfungserfolg

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Oracle 1Z0-1127-25 Prüfungsplan:

Thema	Einzelheiten
Thema 1	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 2	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 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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1Z0-1127-25 Zertifizierungsfragen, Oracle 1Z0-1127-25 PrüfungFragen

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Oracle Cloud Infrastructure 2025 Generative AI Professional 1Z0-1127-25 Prüfungsfragen mit Lösungen (Q28-Q33):

28. Frage
An AI development company is working on an AI-assisted chatbot for a customer, which happens to be an online retail company. The goal is to create an assistant that can best answer queries regarding the company policies as well as retain the chat history throughout a session. Considering the capabilities, which type of model would be the best?

A. An LLM enhanced with Retrieval-Augmented Generation (RAG) for dynamic information retrieval and response generation.
B. A pre-trained LLM model from Cohere or OpenAI.
C. A keyword search-based AI that responds based on specific keywords identified in customer queries.
D. An LLM dedicated to generating text responses without external data integration.

Antwort: A

Begründung:
Comprehensive and Detailed In-Depth Explanation=
For a chatbot needing to answer policy queries (requiring up-to-date, specific data) and retain chat history (context awareness), an LLM with RAG is ideal. RAG integrates external data (e.g., policy documents) via retrieval and supports memory for session-long context, making Option B correct. Option A (keyword search) lacks reasoning and context retention. Option C (standalone LLM) can't dynamically fetch policy data. Option D (pre-trained LLM) is too vague and lacks RAG's capabilities. RAG meets both requirements effectively.
OCI 2025 Generative AI documentation likely highlights RAG for dynamic, context-aware applications.

29. 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.

30. Frage
What does accuracy measure in the context of fine-tuning results for a generative model?

A. The number of predictions a model makes, regardless of whether they are correct or incorrect
B. How many predictions the model made correctly out of all the predictions in an evaluation
C. The proportion of incorrect predictions made by the model during an evaluation
D. The depth of the neural network layers used in the model

Antwort: B

Begründung:
Comprehensive and Detailed In-Depth Explanation=
Accuracy in fine-tuning measures the proportion of correct predictions (e.g., matching expected outputs) out of all predictions made during evaluation, reflecting model performance-Option C is correct. Option A (total predictions) ignores correctness. Option B (incorrect proportion) is the inverse-error rate. Option D (layer depth) is unrelated to accuracy. Accuracy is a standard metric for generative tasks.OCI 2025 Generative AI documentation likely defines accuracy under fine-tuning evaluation metrics.

31. Frage
Which is NOT a typical use case for LangSmith Evaluators?

A. Measuring coherence of generated text
B. Detecting bias or toxicity
C. Aligning code readability
D. Evaluating factual accuracy of outputs

Antwort: C

Begründung:
Comprehensive and Detailed In-Depth Explanation=
LangSmith Evaluators assess LLM outputs for qualities like coherence (A), factual accuracy (C), and bias/toxicity (D), aiding development and debugging. Aligning code readability (B) pertains to software engineering, not LLM evaluation, making it the odd one out-Option B is correct as NOT a use case. Options A, C, and D align with LangSmith's focus on text quality and ethics.
OCI 2025 Generative AI documentation likely lists LangSmith Evaluator use cases under evaluation tools.

32. Frage
What is the role of temperature in the decoding process of a Large Language Model (LLM)?

A. To determine the number of words to generate in a single decoding step
B. To decide to which part of speech the next word should belong
C. To adjust the sharpness of probability distribution over vocabulary when selecting the next word
D. To increase the accuracy of the most likely word in the vocabulary

Antwort: C

Begründung:
Comprehensive and Detailed In-Depth Explanation=
Temperature is a hyperparameter in the decoding process of LLMs that controls the randomness of word selection by modifying the probability distribution over the vocabulary. A lower temperature (e.g., 0.1) sharpens the distribution, making the model more likely to select the highest-probability words, resulting in more deterministic and focused outputs. A higher temperature (e.g., 2.0) flattens the distribution, increasing the likelihood of selecting less probable words, thus introducing more randomness and creativity. Option D accurately describes this role. Option A is incorrect because temperature doesn't directly increase accuracy but influences output diversity. Option B is unrelated, as temperature doesn't dictate the number of words generated. Option C is also incorrect, as part-of-speech decisions are not directly tied to temperature but to the model's learned patterns.
General LLM decoding principles, likely covered in OCI 2025 Generative AI documentation under decoding parameters like temperature.

33. Frage
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