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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	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 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.
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 (Q27-Q32):

NEW QUESTION # 27
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

A. By excluding transformer layers from the fine-tuning process entirely
B. By allowing updates across all layers of the model
C. By incorporating additional layers to the base model
D. By restricting updates to only a specific group of transformer layers

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning enhances efficiency by updating only a small subset of transformer layers or parameters (e.g., via adapters), reducing computational load-Option D is correct. Option A (adding layers) increases complexity, not efficiency. Option B (all layers) describes Vanilla fine-tuning. Option C (excluding layers) is false-T-Few updates, not excludes. This selective approach optimizes resource use.
OCI 2025 Generative AI documentation likely details T-Few under PEFT methods.

NEW QUESTION # 28
What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?

A. The process of training the model on k different tasks simultaneously to improve its versatility
B. Providing the exact k words in the prompt to guide the model's response
C. Limiting the model to only k possible outcomes or answers for a given task
D. Explicitly providing k examples of the intended task in the prompt to guide the model's output

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
"k-shot prompting" (e.g., few-shot) involves providing k examples of a task in the prompt to guide the LLM's output via in-context learning, without additional training. This makes Option B correct. Option A (k words) misinterprets-examples, not word count, matter. Option C (training) confuses prompting with fine-tuning. Option D (k outcomes) is unrelated-k refers to examples, not limits. k-shot leverages pre-trained knowledge efficiently.
OCI 2025 Generative AI documentation likely covers k-shot prompting under prompt engineering techniques.

NEW QUESTION # 29
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

A. When you want to optimize the model without any instructions
B. When the LLM already understands the topics necessary for text generation
C. When the LLM requires access to the latest data for generating outputs
D. When the LLM does not perform well on a task and the data for prompt engineering is too large

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning is suitable when an LLM underperforms on a specific task and prompt engineering alone isn't feasible due to large, task-specific data that can't be efficiently included in prompts. This adjusts the model's weights, making Option B correct. Option A suggests no customization is needed. Option C favors RAG for latest data, not fine-tuning. Option D is vague-fine-tuning requires data and goals, not just optimization without direction. Fine-tuning excels with substantial task-specific data.
OCI 2025 Generative AI documentation likely outlines fine-tuning use cases under customization strategies.

NEW QUESTION # 30
What happens if a period (.) is used as a stop sequence in text generation?

A. The model stops generating text after it reaches the end of the current paragraph.
B. The model generates additional sentences to complete the paragraph.
C. The model stops generating text after it reaches the end of the first sentence, even if the token limit is much higher.
D. The model ignores periods and continues generating text until it reaches the token limit.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A stop sequence in text generation (e.g., a period) instructs the model to halt generation once it encounters that token, regardless of the token limit. If set to a period, the model stops after the first sentence ends, making Option D correct. Option A is false, as stop sequences are enforced. Option B contradicts the stop sequence's purpose. Option C is incorrect, as it stops at the sentence level, not paragraph.
OCI 2025 Generative AI documentation likely explains stop sequences under text generation parameters.

NEW QUESTION # 31
How are documents usually evaluated in the simplest form of keyword-based search?

A. By the complexity of language used in the documents
B. Based on the number of images and videos contained in the documents
C. According to the length of the documents
D. Based on the presence and frequency of the user-provided keywords

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In basic keyword-based search, documents are evaluated by matching user-provided keywords, with relevance often determined by their presence and frequency (e.g., term frequency in TF-IDF). This makes Option C correct. Option A (language complexity) is unrelated to simple keyword search. Option B (multimedia) isn't considered in text-based keyword methods. Option D (length) may influence scoring indirectly but isn't the primary metric. Keyword search prioritizes exact matches.
OCI 2025 Generative AI documentation likely contrasts keyword search with semantic search under retrieval methods.

NEW QUESTION # 32
......

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New 1Z0-1127-25 Exam Simulator | Test 1Z0-1127-25 Simulator

Oracle 1Z0-1127-25 Exam Syllabus Topics:

Oracle New 1Z0-1127-25 Exam Simulator - Realistic Test Oracle Cloud Infrastructure 2025 Generative AI Professional Simulator

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q27-Q32):

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