PASS GUARANTEED QUIZ 2025 ORACLE 1Z0-1127-25: ORACLE CLOUD INFRASTRUCTURE 2025 GENERATIVE AI PROFESSIONAL PASS-SURE DETAILED STUDY PLAN

Pass Guaranteed Quiz 2025 Oracle 1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generative AI Professional Pass-Sure Detailed Study Plan

Pass Guaranteed Quiz 2025 Oracle 1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generative AI Professional Pass-Sure Detailed Study Plan

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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 (Q64-Q69):

NEW QUESTION # 64
What does a cosine distance of 0 indicate about the relationship between two embeddings?

  • A. They are unrelated
  • B. They have the same magnitude
  • C. They are similar in direction
  • D. They are completely dissimilar

Answer: C

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 # 65
How does the structure of vector databases differ from traditional relational databases?

  • A. It is not optimized for high-dimensional spaces.
  • B. It is based on distances and similarities in a vector space.
  • C. It uses simple row-based data storage.
  • D. It stores data in a linear or tabular format.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Vector databases store data as high-dimensional vectors (embeddings) and are optimized for similarity searches using metrics like cosine distance, unlike relational databases, which use tabular rows and columns for structured data. This makes Option D correct. Options A and C describerelational databases, not vector ones. Option B is false, as vector databases are specifically designed for high-dimensional spaces. Vector databases excel in semantic search and LLM integration.
OCI 2025 Generative AI documentation likely contrasts vector and relational databases under data storage.


NEW QUESTION # 66
Which is NOT a typical use case for LangSmith Evaluators?

  • A. Measuring coherence of generated text
  • B. Evaluating factual accuracy of outputs
  • C. Detecting bias or toxicity
  • D. Aligning code readability

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangSmith Evaluators assess LLM outputs for qualities like coherence (A), factual accuracy (C), and bias/toxicity (D), aiding development and debugging. Aligning code readability (B) pertains to software engineering, not LLM evaluation, making it the odd one out-Option B is correct as NOT a use case. Options A, C, and D align with LangSmith's focus on text quality and ethics.
OCI 2025 Generative AI documentation likely lists LangSmith Evaluator use cases under evaluation tools.


NEW QUESTION # 67
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

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

Answer: A

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 # 68
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 ignores periods and continues generating text until it reaches the token limit.
  • C. The model generates additional sentences to complete the paragraph.
  • D. The model stops generating text after it reaches the end of the first sentence, even if the token limit is much higher.

Answer: D

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 # 69
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