HOT 1Z0-1127-24 LATEST EXAM | PROFESSIONAL 1Z0-1127-24: ORACLE CLOUD INFRASTRUCTURE 2024 GENERATIVE AI PROFESSIONAL 100% PASS

Hot 1z0-1127-24 Latest Exam | Professional 1z0-1127-24: Oracle Cloud Infrastructure 2024 Generative AI Professional 100% Pass

Hot 1z0-1127-24 Latest Exam | Professional 1z0-1127-24: Oracle Cloud Infrastructure 2024 Generative AI Professional 100% Pass

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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
  • 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.
Topic 3
  • 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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q21-Q26):

NEW QUESTION # 21
What does a dedicated RDMA cluster network do during model fine-tuning and inference?

  • A. It increases G PU memory requirements for model deployment.
  • B. It leads to higher latency in model inference.
  • C. It enables the deployment of multiple fine-tuned models.
  • D. It limits the number of fine-tuned model deployable on the same GPU cluster.

Answer: C

Explanation:
A dedicated RDMA (Remote Direct Memory Access) cluster network is crucial during model fine-tuning and inference because it facilitates high-speed, low-latency communication between GPUs. This capability is essential for scaling up the deployment of multiple fine-tuned models across a GPU cluster.
RDMA allows data to be transferred directly between the memory of different computers without involving the CPU, leading to significantly reduced latency and higher throughput. This efficiency is particularly important in the context of fine-tuning and deploying large language models, where the speed and efficiency of data transfer can impact overall performance and scalability.
By enabling fast and efficient communication, a dedicated RDMA cluster network supports the deployment of multiple fine-tuned models on the same GPU cluster, enhancing both flexibility and scalability in handling various AI workloads.
Reference
Oracle Cloud Infrastructure (OCI) documentation on RDMA cluster networks Technical resources on the benefits of RDMA in high-performance computing environments


NEW QUESTION # 22
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?

  • A. A Large Language Model based agent that focuses on generating textual responses
  • B. A diffusion model that specializes in producing complex outputs.
  • C. A language model that operates on a token-by-token output basis
  • D. A Retrieval Augmented Generation (RAG) model that uses text as input and output

Answer: D


NEW QUESTION # 23
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

  • A. It assigns a penalty to frequently occurring tokens to reduce repetitive text.
  • B. It specifies a string that tells the model to stop generating more content
  • C. It com rob the randomness of the model* output, affecting its creativity.
  • D. It determines the maximum number of tokens the model can generate per response.

Answer: B

Explanation:
The "stop sequence" parameter in the OCI Generative AI Generation models is used to specify a string that signals the model to stop generating further content. When the model encounters this string during the generation process, it terminates the response. This parameter is useful for controlling the length and content of the generated text, ensuring that the output meets specific requirements or constraints.
Reference
OCI Generative AI service documentation
General principles of sequence generation in AI models


NEW QUESTION # 24
What is the primary purpose of LangSmith Tracing?

  • A. To generate test cases for language models
  • B. To analyze the reasoning process of language
  • C. To monitor the performance of language models
  • D. To debug issues in language model outputs

Answer: B


NEW QUESTION # 25
Which LangChain component is responsible for generating the linguistic output in a chatbot system?

  • A. Vector Stores
  • B. LangChain Application
  • C. LLMs
  • D. Document Loaders

Answer: C

Explanation:
LangChain is an open-source framework that helps integrate Large Language Models (LLMs) into applications. In a chatbot system, the LLM (Large Language Model) component is responsible for generating linguistic output, as it processes user inputs and generates human-like responses.
Key components of LangChain include:
Document Loaders - Responsible for extracting and processing external data sources before passing them to the LLM.
Vector Stores - Used for storing and retrieving vector embeddings of documents for semantic search and similarity retrieval.
LLMs (Large Language Models) - This is the core component responsible for understanding prompts and generating text-based outputs in a chatbot.
LangChain Applications - The overall framework that connects all components but does not directly generate text.
???? Oracle Generative AI Reference:
Oracle supports LLM-driven chatbots and enterprise AI solutions, utilizing frameworks like LangChain to enhance AI capabilities.


NEW QUESTION # 26
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