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100% Pass 2025 Perfect 1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generative AI Professional Latest Exam Experience

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Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic
Details

Topic 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.

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

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.

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q66-Q71):

NEW QUESTION # 66
Which is a distinguishing feature of "Parameter-Efficient Fine-Tuning (PEFT)" as opposed to classic "Fine-tuning" in Large Language Model training?

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

Answer: B

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 # 67
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of-Thought, Least-to-Most, or Step-Back prompting technique:

A. "Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question."
B. "Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50."
C. "To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, we'll explore how they trap heat in the Earth's atmosphere."A. 1: Step-Back, 2: Chain-of-Thought, 3: Least-to-MostB. 1: Least-to-Most, 2: Chain-of-Thought, 3: Step-BackC. 1: Chain-of-Thought, 2: Step-Back, 3: Least-to-MostD. 1: Chain-of-Thought, 2: Least-to-Most, 3: Step-Back

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt 1: Shows intermediate steps (3 × 4 = 12, then 12 ÷ 4 = 3 sets, $200 ÷ $50 = 4)-Chain-of-Thought.
Prompt 2: Steps back to a simpler problem before the full one-Step-Back.
Prompt 3: OCI 2025 Generative AI documentation likely defines these under prompting strategies.

NEW QUESTION # 68
When does a chain typically interact with memory in a run within the LangChain framework?

A. Continuously throughout the entire chain execution process
B. After user input but before chain execution, and again after core logic but before output
C. Before user input and after chain execution
D. Only after the output has been generated

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, a chain interacts with memory after receiving user input (to retrieve context) but before execution (to inform processing), and again after core logic (to update memory) but before output (to maintain state). This makes Option C correct. Option A misses pre-execution context. Option B misplaces timing. Option D overstates-interaction is at specific stages, not continuous. Memory ensures context-aware responses.
OCI 2025 Generative AI documentation likely details memory interaction under LangChain chain execution.

NEW QUESTION # 69
In which scenario is soft prompting appropriate compared to other training styles?

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

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Soft prompting adds trainable parameters (soft prompts) to adapt an LLM without retraining its core weights, ideal for low-resource customization without task-specific data. This makes Option C correct. Option A suits fine-tuning. Option B may require more than soft prompting (e.g., domain fine-tuning). Option D describes pretraining, not soft prompting. Soft prompting is efficient for specific adaptations.
OCI 2025 Generative AI documentation likely discusses soft prompting under PEFT methods.

NEW QUESTION # 70
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

A. The token is more likely to follow the current token.
B. The token will be the only one considered in the next generation step.
C. The token is unrelated to the current token and will not be used.
D. The token is less likely to follow the current token.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In "Show Likelihoods," a higher number (probability score) indicates a token's greater likelihood of following the current token, reflecting the model's prediction confidence-Option B is correct. Option A (less likely) is the opposite. Option C (unrelated) misinterprets-likelihood ties tokens contextually. Option D (only one) assumes greedy decoding, not the feature's purpose. This helps users understand model preferences.
OCI 2025 Generative AI documentation likely explains "Show Likelihoods" under token generation insights.

NEW QUESTION # 71
......

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