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Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic	Details
Topic 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.
Topic 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.
Topic 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.
Topic 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 Sample Questions (Q30-Q35):

NEW QUESTION # 30
In which scenario is soft prompting especially appropriate compared to other training styles?

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

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Soft prompting (e.g., prompt tuning) involves adding trainable parameters (soft prompts) to an LLM's input while keeping the model's weights frozen, adapting it to tasks without task-specific retraining. This is efficient when fine-tuning or large datasets aren't feasible, making Option C correct. Option A suits full fine-tuning, not soft prompting, which avoids extensive labeled data needs. Option B could apply, but domain adaptation often requires more than soft prompting (e.g., fine-tuning). Option D describes continued pretraining, not soft prompting. Soft prompting excels in low-resource customization.
OCI 2025 Generative AI documentation likely discusses soft prompting under parameter-efficient methods.

NEW QUESTION # 31
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

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

Answer: A

Explanation:
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.

NEW QUESTION # 32
What does a cosine distance of 0 indicate about the relationship between two embeddings?

A. They are completely dissimilar
B. They have the same magnitude
C. They are unrelated
D. They are similar in direction

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Cosine distance measures the angle between two vectors, where 0 means the vectors point in the same direction (cosine similarity = 1), indicating high similarity in embeddings' semantic content-Option C is correct. Option A (dissimilar) aligns with a distance of 1. Option B is vague-directional similarity matters. Option D (magnitude) isn't relevant-cosine ignores magnitude. This is key for semantic comparison.
OCI 2025 Generative AI documentation likely explains cosine distance under vector database metrics.

NEW QUESTION # 33
Which is a distinguishing feature of "Parameter-Efficient Fine-Tuning (PEFT)" as opposed to classic "Fine-tuning" in Large Language Model training?

A. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
B. PEFT modifies all parameters and is typically used when no training data exists.
C. PEFT involves only a few or new parameters and uses labeled, task-specific data.
D. PEFT does not modify any parameters but uses soft prompting with unlabeled data.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
PEFT (e.g., LoRA, T-Few) updates a small subset of parameters (often new ones) using labeled, task-specific data, unlike classic fine-tuning, which updates all parameters-Option A is correct. Option B reverses PEFT's efficiency. Option C (no modification) fits soft prompting, not all PEFT. Option D (all parameters) mimics classic fine-tuning. PEFT reduces resource demands.
OCI 2025 Generative AI documentation likely contrasts PEFT and fine-tuning under customization methods.

NEW QUESTION # 34
Given the following code:
PromptTemplate(input_variables=["human_input", "city"], template=template) Which statement is true about PromptTemplate in relation to input_variables?

A. PromptTemplate requires a minimum of two variables to function properly.
B. PromptTemplate is unable to use any variables.
C. PromptTemplate supports any number of variables, including the possibility of having none.
D. PromptTemplate can support only a single variable at a time.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, PromptTemplate supports any number of input_variables (zero, one, or more), allowing flexible prompt design-Option C is correct. The example shows two, but it's not a requirement. Option A (minimum two) is false-no such limit exists. Option B (single variable) is too restrictive. Option D (no variables) contradicts its purpose-variables are optional but supported. This adaptability aids prompt engineering.
OCI 2025 Generative AI documentation likely covers PromptTemplate under LangChain prompt design.

NEW QUESTION # 35
......

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1Z0-1127-25 Exam Actual Questions & 1Z0-1127-25 Practice Questions

Oracle 1Z0-1127-25 Exam Syllabus Topics:

Pass Guaranteed Quiz Oracle 1Z0-1127-25 Marvelous Exam Actual Questions

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q30-Q35):

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