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1Z0-1127-25 Exam Preparation - Exam 1Z0-1127-25 Voucher

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Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic
Details

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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2025 1Z0-1127-25 – 100% Free Exam Preparation | Pass-Sure Exam Oracle Cloud Infrastructure 2025 Generative AI Professional Voucher

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q45-Q50):

NEW QUESTION # 45
Which statement best describes the role of encoder and decoder models in natural language processing?

A. Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.
B. Encoder models and decoder models both convert sequences of words into vector representations without generating new text.
C. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.
D. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to generate a sequence of words.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In NLP (e.g., transformers), encoders convert input text into a vector representation (encoding meaning), while decoders generate text from such vectors (e.g., in translation or generation). This makes Option C correct. Option A is false-decoders generate text. Option B reverses roles-encoders don't predict next words, and decoders don't encode. Option D oversimplifies-encoders handle text, not just numbers. This is the foundation of seq2seq models.
OCI 2025 Generative AI documentation likely explains encoder-decoder roles under model architecture.

NEW QUESTION # 46
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. Step-Back Prompting
D. In-context Learning

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 # 47
What does the Ranker do in a text generation system?

A. It generates the final text based on the user's query.
B. It evaluates and prioritizes the information retrieved by the Retriever.
C. It sources information from databases to use in text generation.
D. It interacts with the user to understand the query better.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In systems like RAG, the Ranker evaluates and sorts the information retrieved by the Retriever (e.g., documents or snippets) based on relevance to the query, ensuring the most pertinent data is passed to the Generator. This makes Option C correct. Option A is the Generator's role. Option B describes the Retriever. Option D is unrelated, as the Ranker doesn't interact with users but processes retrieved data. The Ranker enhances output quality by prioritizing relevant content.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.

NEW QUESTION # 48
What does the Loss metric indicate about a model's predictions?

A. Loss measures the total number of predictions made by a model.
B. Loss describes the accuracy of the right predictions rather than the incorrect ones.
C. Loss is a measure that indicates how wrong the model's predictions are.
D. Loss indicates how good a prediction is, and it should increase as the model improves.

Answer: C

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 # 49
Which is a distinctive feature of GPUs in Dedicated AI Clusters used for generative AI tasks?

A. The GPUs allocated for a customer's generative AI tasks are isolated from other GPUs.
B. GPUs are used exclusively for storing large datasets, not for computation.
C. Each customer's GPUs are connected via a public Internet network for ease of access.
D. GPUs are shared with other customers to maximize resource utilization.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In Dedicated AI Clusters (e.g., in OCI), GPUs are allocated exclusively to a customer for their generative AI tasks, ensuring isolation for security, performance, and privacy. This makes Option B correct. Option A describes shared resources, not dedicated clusters. Option C is false, as GPUs are for computation, not storage. Option D is incorrect, as public Internet connections would compromise security and efficiency.
OCI 2025 Generative AI documentation likely details GPU isolation under DedicatedAI Clusters.

NEW QUESTION # 50
......

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Rob Green Rob Green

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