1Z0-1127-25 Exam Questions - Oracle Cloud Infrastructure 2025 Generative AI Professional Exam Tests & 1Z0-1127-25 Test Guide

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Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 (Q41-Q46):

NEW QUESTION # 41
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

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

Answer: D

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 # 42
Which statement best describes the role of encoder and decoder models in natural language processing?

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

Answer: B

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 # 43
How are documents usually evaluated in the simplest form of keyword-based search?

A. Based on the presence and frequency of the user-provided keywords
B. By the complexity of language used in the documents
C. According to the length of the documents
D. Based on the number of images and videos contained in the documents

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In basic keyword-based search, documents are evaluated by matching user-provided keywords, with relevance often determined by their presence and frequency (e.g., term frequency in TF-IDF). This makes Option C correct. Option A (language complexity) is unrelated to simple keyword search. Option B (multimedia) isn't considered in text-based keyword methods. Option D (length) may influence scoring indirectly but isn't the primary metric. Keyword search prioritizes exact matches.
OCI 2025 Generative AI documentation likely contrasts keyword search with semantic search under retrieval methods.

NEW QUESTION # 44
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

A. Translation models
B. Embedding models
C. Generation models
D. Summarization models

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
OCI Generative AI typically offers pretrained models for summarization (A), generation (B), and embeddings (D), aligning with common generative tasks. Translation models (C) are less emphasized in generative AI services, often handled by specialized NLP platforms, making C the NOT category. While possible, translation isn't a core OCI generative focus based on standard offerings.
OCI 2025 Generative AI documentation likely lists model categories under pretrained options.

NEW QUESTION # 45
In which scenario is soft prompting especially appropriate compared to other training styles?

A. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training.
B. When there is a significant amount of labeled, task-specific data available.
C. When the model requires continued pre-training on unlabeled data.
D. When the model needs to be adapted to perform well in a different domain it was not originally trained on.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Soft prompting (e.g., prompt tuning) involves adding trainable parameters (soft prompts) to an LLM's input while keeping the model's weights frozen, adapting it to tasks without task-specific retraining. This is efficient when fine-tuning or large datasets aren't feasible, making Option C correct. Option A suits full fine-tuning, not soft prompting, which avoids extensive labeled data needs. Option B could apply, but domain adaptation often requires more than soft prompting (e.g., fine-tuning). Option D describes continued pretraining, not soft prompting. Soft prompting excels in low-resource customization.
OCI 2025 Generative AI documentation likely discusses soft prompting under parameter-efficient methods.

NEW QUESTION # 46
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1Z0-1127-25 Exam Questions - Oracle Cloud Infrastructure 2025 Generative AI Professional Exam Tests & 1Z0-1127-25 Test Guide

Oracle 1Z0-1127-25 Exam Syllabus Topics:

Reliable 1Z0-1127-25 Test Question - Latest 1Z0-1127-25 Braindumps Pdf

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q41-Q46):

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