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[General] 1Z0-1127-25 test questions & 1Z0-1127-25 pass king & 1Z0-1127-25 test en

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【General】 1Z0-1127-25 test questions & 1Z0-1127-25 pass king & 1Z0-1127-25 test en

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Oracle 1Z0-1127-25 Exam Syllabus Topics:
TopicDetails
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
  • 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
  • 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 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.

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q11-Q16):NEW QUESTION # 11
An LLM emits intermediate reasoning steps as part of its responses. Which of the following techniques is being utilized?
  • A. Least-to-Most Prompting
  • B. Chain-of-Thought
  • C. In-context Learning
  • D. Step-Back Prompting
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Chain-of-Thought (CoT) prompting encourages an LLM to emit intermediate reasoning steps before providing a final answer, improving performance on complex tasks by mimicking human reasoning. This matches the scenario, making Option D correct. Option A (In-context Learning) involves learning from examples in the prompt, not necessarily reasoning steps. Option B (Step-Back Prompting) involves reframing the problem, not emitting steps. Option C (Least-to-Most Prompting) breaks tasks into subtasks but doesn't focus on intermediate reasoning explicitly. CoT is widely recognized for reasoning tasks.
OCI 2025 Generative AI documentation likely covers Chain-of-Thought under advanced prompting techniques.

NEW QUESTION # 12
When does a chain typically interact with memory in a run within the LangChain framework?
  • A. Before user input and after chain execution
  • B. Only after the output has been generated
  • C. After user input but before chain execution, and again after core logic but before output
  • D. Continuously throughout the entire chain execution process
Answer: C
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 # 13
What does a cosine distance of 0 indicate about the relationship between two embeddings?
  • A. They are completely dissimilar
  • B. They are unrelated
  • C. They have the same magnitude
  • 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 # 14
What does the Loss metric indicate about a model's predictions?
  • A. Loss indicates how good a prediction is, and it should increase as the model improves.
  • B. Loss describes the accuracy of the right predictions rather than the incorrect ones.
  • C. Loss measures the total number of predictions made by a model.
  • D. Loss is a measure that indicates how wrong the model's predictions are.
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Loss is a metric that quantifies the difference between a model's predictions and the actual target values, indicating how incorrect (or "wrong") the predictions are. Lower loss means better performance, making Option B correct. Option A is false-loss isn't about prediction count. Option C is incorrect-loss decreases as the model improves, not increases. Option D is wrong-loss measures overall error, not just correct predictions. Loss guides training optimization.
OCI 2025 Generative AI documentation likely defines loss under model training and evaluation metrics.

NEW QUESTION # 15
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?
  • A. It selectively updates only a fraction of the model's weights.
  • B. It increases the training time as compared to Vanilla fine-tuning.
  • C. It does not update any weights but restructures the model architecture.
  • D. It updates all the weights of the model uniformly.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning, a Parameter-Efficient Fine-Tuning (PEFT) method, updates only a small fraction of an LLM's weights, reducing computational cost and overfitting risk compared to Vanilla fine-tuning (all weights). This makes Option C correct. Option A describes Vanilla fine-tuning. Option B is false-T-Few updates weights, not architecture. Option D is incorrect-T-Few typically reduces training time. T-Few optimizes efficiency.
OCI 2025 Generative AI documentation likely highlights T-Few under fine-tuning options.

NEW QUESTION # 16
......
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