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2025 1Z0-1127-25–100% Free Valid Exam Pdf | Perfect Test Oracle Cloud Infrastructure 2025 Generative AI Professional Objectives Pdf

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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

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

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 (Q50-Q55):

NEW QUESTION # 50
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 increase the accuracy of the most likely word in the vocabulary
C. To decide to which part of speech the next word should belong
D. To adjust the sharpness of probability distribution over vocabulary when selecting the next word

Answer: D

Explanation:
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.

NEW QUESTION # 51
What do prompt templates use for templating in language model applications?

A. Python's lambda functions
B. Python's list comprehension syntax
C. Python's str.format syntax
D. Python's class and object structures

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt templates in LLM applications (e.g., LangChain) typically use Python's str.format() syntax to insert variables into predefined string patterns (e.g., "Hello, {name}!"). This makes Option B correct. Option A (list comprehension) is for list operations, not templating. Option C (lambda functions) defines functions, not templates. Option D (classes/objects) is overkill-templates are simpler constructs. str.format() ensures flexibility and readability.
OCI 2025 Generative AI documentation likely mentions str.format() under prompt template design.

NEW QUESTION # 52
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

Answer: A

Explanation:
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.

NEW QUESTION # 53
Which role does a "model endpoint" serve in the inference workflow of the OCI Generative AI service?

A. Updates the weights of the base model during the fine-tuning process
B. Serves as a designated point for user requests and model responses
C. Hosts the training data for fine-tuning custom models
D. Evaluates the performance metrics of the custom models

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A "model endpoint" in OCI's inference workflow is an API or interface where users send requests and receive responses from a deployed model-Option B is correct. Option A (weight updates) occurs during fine-tuning, not inference. Option C (metrics) is for evaluation, not endpoints. Option D (training data) relates to storage, not inference. Endpoints enable real-time interaction.
OCI 2025 Generative AI documentation likely describes endpoints under inference deployment.

NEW QUESTION # 54
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. Limiting the model to only k possible outcomes or answers for a given task
C. Providing the exact k words in the prompt to guide the model's response
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 # 55
......

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