2025 UNPARALLELED 1Z0-1127-24 RELIABLE GUIDE FILES HELP YOU PASS 1Z0-1127-24 EASILY

2025 Unparalleled 1z0-1127-24 Reliable Guide Files Help You Pass 1z0-1127-24 Easily

2025 Unparalleled 1z0-1127-24 Reliable Guide Files Help You Pass 1z0-1127-24 Easily

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Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Building an LLM Application with OCI Generative AI Service: For AI Engineers, this section covers Retrieval Augmented Generation (RAG) concepts, vector database concepts, and semantic search concepts. It also focuses on deploying an LLM, tracing and evaluating an LLM, and building an LLM application with RAG and LangChain.
Topic 2
  • Fundamentals of Large Language Models (LLMs): For AI developers and Cloud Architects, this topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.
Topic 3
  • Using OCI Generative AI Service: For AI Specialists, this section covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on the fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q42-Q47):

NEW QUESTION # 42
What does "k-shot prompting* refer to when using Large Language Models for task-specific applications?

  • A. Explicitly providing k examples of the intended task in the prompt to guide the models output
  • B. The process of training the model on k different tasks simultaneously to improve its versatility
  • C. Limiting the model to only k possible outcomes or answers for a given task
  • D. Providing the exact k words in the prompt to guide the model's response

Answer: A

Explanation:
K-shot prompting refers to providing the language model with k examples of the task at hand within the prompt. These examples help guide the model's understanding and output by demonstrating the desired format and approach. This technique is used to improve the model's performance on specific tasks by showing it how to handle similar situations.
Reference
Research papers on few-shot learning and prompting techniques
Technical documentation on using examples in prompts for large language models


NEW QUESTION # 43
What is LangChain?

  • A. A Python library for building applications with Large Language Models
  • B. A Java library for text summarization
  • C. A JavaScript library for natural language processing
  • D. A Ruby library for text generation

Answer: A

Explanation:
LangChain is an open-source framework that helps developers integrate Large Language Models (LLMs) into applications. It simplifies working with AI by handling data retrieval, memory, agents, and pipelines.
Key Features of LangChain:
Works with multiple LLMs, including OpenAI, Hugging Face, and enterprise solutions.
Simplifies AI-powered applications, such as chatbots, document summarization, and RAG-based search.
Provides tools for vector storage, indexing, and retrieval.
Enhances AI workflows by combining LLMs with external data sources.
Why Other Options Are Incorrect:
(A) JavaScript library - LangChain is written in Python, not JavaScript.
(B) Ruby library - LangChain is not a Ruby framework.
(D) Java library - LangChain is not Java-based.
???? Oracle Generative AI Reference:
Oracle integrates LangChain for LLM-based applications in document search, AI chatbots, and workflow automation.


NEW QUESTION # 44
What does "k-shot prompting* refer to when using Large Language Models for task-specific applications?

  • A. Explicitly providing k examples of the intended task in the prompt to guide the models output
  • B. The process of training the model on k different tasks simultaneously to improve its versatility
  • C. Limiting the model to only k possible outcomes or answers for a given task
  • D. Providing the exact k words in the prompt to guide the model's response

Answer: A


NEW QUESTION # 45
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?

  • A. By loading the entire model into G PU memory for efficient processing
  • B. By allocating separate GPUS for each model instance
  • C. By optimizing GPIJ memory utilization for each model's unique para
  • D. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs

Answer: D

Explanation:
The architecture of dedicated AI clusters contributes to minimizing GPU memory overhead for fine-tuned model inference by sharing base model weights across multiple fine-tuned models on the same group of GPUs. This approach allows different fine-tuned models to leverage the shared base model weights, reducing the memory requirements and enabling efficient use of GPU resources. By not duplicating the base model weights for each fine-tuned model, the system can handle more models simultaneously with lower memory overhead.
Reference
Technical documentation on AI cluster architectures
Research articles on optimizing GPU memory utilization in model inference


NEW QUESTION # 46
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

  • A. Stored in Key Management service
  • B. Stored in an unencrypted form in Object Storage
  • C. Stored in Object Storage encrypted by default
  • D. Shared among multiple customers for efficiency

Answer: C

Explanation:
Fine-tuned customer models in the OCI Generative AI service are stored in Object Storage, and they are encrypted by default. This encryption ensures strong data privacy and security by protecting the model data from unauthorized access. Using encrypted storage is a key measure in safeguarding sensitive information and maintaining compliance with security standards.
Reference
OCI documentation on data storage and security practices
Technical details on encryption and data privacy in OCI services


NEW QUESTION # 47
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