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Kostenlose gültige Prüfung Oracle 1Z0-1127-25 Sammlung - Examcollection

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Die Forschungsmaterialien haben gezeigt, dass es schwierig ist, die Oracle 1Z0-1127-25 Zertifizierungsprüfung zu bestehen. Unser ZertFragen hat erfahrungsreiche IT-Experten, die durch harte Arbeit die neuesten Schulungsunterlagen zur Oracle 1Z0-1127-25 Zertifizierungsprüfung bearbeitet haben. Unser ZertFragen hat die besten Ressourcen, die Ihnen beim Bestehen der Oracle 1Z0-1127-25 Prüfung helfen. Sie enthalten sowohl Fragen, als auch Antworten. Sie brauchen sich nicht so viel Mühe dafür auszugeben und können trotzdem eine hohe Note in der Prüfung bekommen. Wählen Sie doch die Schulungsunterlagen zur Oracle 1Z0-1127-25 Zertifizierungsprüfung, die Ihnen sehr helfen können.

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

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

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.

>> 1Z0-1127-25 Vorbereitung <<

1Z0-1127-25 Fragen Und Antworten & 1Z0-1127-25 Musterprüfungsfragen

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Oracle Cloud Infrastructure 2025 Generative AI Professional 1Z0-1127-25 Prüfungsfragen mit Lösungen (Q30-Q35):

30. Frage
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?

A. The level of incorrectness in the model's predictions, with lower values indicating better performance
B. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
C. The improvement in accuracy achieved by the model during training on the user-uploaded dataset
D. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation

Antwort: A

Begründung:
Comprehensive and Detailed In-Depth Explanation=
Loss measures the discrepancy between a model's predictions and true values, with lower values indicating better fit-Option D is correct. Option A (accuracy difference) isn't loss-it's a derived metric. Option B (error percentage) is closer to error rate, not loss. Option C (accuracy improvement) is a training outcome, not loss's definition. Loss is a fundamental training signal.
OCI 2025 Generative AI documentation likely defines loss under fine-tuning metrics.

31. Frage
Why is normalization of vectors important before indexing in a hybrid search system?

A. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.
B. It significantly reduces the size of the database.
C. It converts all sparse vectors to dense vectors.
D. It ensures that all vectors represent keywords only.

Antwort: A

Begründung:
Comprehensive and Detailed In-Depth Explanation=
Normalization scales vectors to unit length, ensuring comparisons (e.g., cosine similarity) reflect directional similarity, not magnitude differences, critical for hybrid search accuracy. This makes Option C correct. Option A is false-vectors represent semantics, not just keywords. Option B (size reduction) isn't the goal. Option D (sparse to dense) is unrelated-normalization adjusts length. Normalized vectors ensure fair similarity metrics.
OCI 2025 Generative AI documentation likely explains normalization under vector preprocessing.

32. Frage
What issue might arise from using small datasets with the Vanilla fine-tuning method in the OCI Generative AI service?

A. Data Leakage
B. Underfitting
C. Model Drift
D. Overfitting

Antwort: D

Begründung:
Comprehensive and Detailed In-Depth Explanation=
Vanilla fine-tuning updates all model parameters, and with small datasets, it can overfit-memorizing the data rather than generalizing-leading to poor performance on unseen data. Option A is correct. Option B (underfitting) is unlikely with full updates-overfitting is the risk. Option C (data leakage) depends on data handling, not size. Option D (model drift) relates to deployment shifts, not training. Small datasets exacerbate overfitting in Vanilla fine-tuning.
OCI 2025 Generative AI documentation likely warns of overfitting under Vanilla fine-tuning limitations.

33. Frage
What is the primary purpose of LangSmith Tracing?

A. To debug issues in language model outputs
B. To monitor the performance of language models
C. To analyze the reasoning process of language models
D. To generate test cases for language models

Antwort: A

Begründung:
Comprehensive and Detailed In-Depth Explanation=
LangSmith Tracing is a tool for debugging and understanding LLM applications by tracking inputs, outputs, and intermediate steps, helping identify issues in complex chains. This makes Option C correct. Option A (test cases) is a secondary use, not primary. Option B (reasoning) overlaps but isn't the core focus-debugging is. Option D (performance) is broader-tracing targets specific issues. It's essential for development transparency.OCI 2025 Generative AI documentation likely covers LangSmith under debugging or monitoring tools.

34. Frage
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

A. T-Few fine-tuning relies on unsupervised learning techniques for annotation.
B. T-Few fine-tuning requires manual annotation of input-output pairs.
C. T-Few fine-tuning involves updating the weights of all layers in the model.
D. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.

Antwort: D

Begründung:
Comprehensive and Detailed In-Depth Explanation=
T-Few, a Parameter-Efficient Fine-Tuning (PEFT) method, uses annotated (labeled) data to selectively update a small fraction of model weights, optimizing efficiency-Option A is correct. Option B is false-manual annotation isn't required; the data just needs labels. Option C (all layers) describes Vanilla fine-tuning, not T-Few. Option D (unsupervised) is incorrect-T-Few typically uses supervised, annotated data. Annotation supports targeted updates.
OCI 2025 Generative AI documentation likely details T-Few's data requirements under fine-tuning processes.

35. Frage
......

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