ORACLE - UPDATED VALID BRAINDUMPS 1Z0-1127-25 FILES

Oracle - Updated Valid Braindumps 1Z0-1127-25 Files

Oracle - Updated Valid Braindumps 1Z0-1127-25 Files

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Oracle 1Z0-1127-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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 (Q13-Q18):

NEW QUESTION # 13
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. "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
  • C. "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."

Answer: B

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 # 14
What is the purpose of Retrievers in LangChain?

  • A. To retrieve relevant information from knowledge bases
  • B. To train Large Language Models
  • C. To break down complex tasks into smaller steps
  • D. To combine multiple components into a single pipeline

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Retrievers in LangChain fetch relevant information (e.g., documents, embeddings) from external knowledge bases (like vector stores) to provide context for LLM responses, especially in RAG setups. This makes Option B correct. Option A (training) is unrelated-Retrievers operate at inference. Option C (task breakdown) pertains to prompting techniques, not retrieval. Option D (pipeline combination) describes chains, not Retrievers specifically. Retrievers enhance context awareness.
OCI 2025 Generative AI documentation likely defines Retrievers under LangChain components.


NEW QUESTION # 15
Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?

  • A. Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data.
  • B. Fine-tuning modifies all parameters using labeled, task-specific data, whereas Parameter Efficient Fine-Tuning updates a few, new parameters also with labeled, task-specific data.
  • C. Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data.
  • D. Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning typically involves updating all parameters of an LLM using labeled, task-specific data to adapt it to a specific task, which is computationally expensive. Parameter Efficient Fine-Tuning (PEFT), such as methods like LoRA (Low-Rank Adaptation), updates only a small subset of parameters (often newly added ones) while still using labeled, task-specific data, making it more efficient. Option C correctly captures this distinction. Option A is wrong because continuous pretraining uses unlabeled data and isn't task-specific. Option B is incorrect as PEFT and Soft Prompting don't modify all parameters, and Soft Prompting typically uses labeled examples indirectly. Option D is inaccurate because continuous pretraining modifies parameters, while SoftPrompting doesn't.
OCI 2025 Generative AI documentation likely discusses Fine-tuning and PEFT under model customization techniques.


NEW QUESTION # 16
What is the main advantage of using few-shot model prompting to customize a Large Language Model (LLM)?

  • A. It significantly reduces the latency for each model request.
  • B. It allows the LLM to access a larger dataset.
  • C. It provides examples in the prompt to guide the LLM to better performance with no training cost.
  • D. It eliminates the need for any training or computational resources.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Few-shot prompting involves providing a few examples in the prompt to guide the LLM's behavior, leveraging its in-context learning ability without requiring retraining or additional computational resources. This makes Option C correct. Option A is false, as few-shot prompting doesn't expand the dataset. Option B overstates the case, as inference still requires resources. Option D is incorrect, as latency isn't significantly affected by few-shot prompting.
OCI 2025 Generative AI documentation likely highlights few-shot prompting in sections on efficient customization.


NEW QUESTION # 17
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

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

Answer: B

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 # 18
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