RELIABLE 1Z0-1127-24 DUMPS EBOOK - STANDARD 1Z0-1127-24 ANSWERS

Reliable 1z0-1127-24 Dumps Ebook - Standard 1z0-1127-24 Answers

Reliable 1z0-1127-24 Dumps Ebook - Standard 1z0-1127-24 Answers

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Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q45-Q50):

NEW QUESTION # 45
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

  • A. Specifies a string that tells the model to stop generating more content
  • B. Assigns a penalty to tokens that have already appeared in the preceding text
  • C. Controls the randomness of the model's output, affecting its creativity
  • D. Determines the maximum number of tokens the model can generate per response

Answer: C


NEW QUESTION # 46
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models(LLMS) fundamentally alter their responses?

  • A. It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.
  • B. It transforms their architecture from a neural network to a traditional database system.
  • C. It enables them to bypass the need for pretraining on large text corpora.
  • D. It limits their ability to understand and generate natural language.

Answer: A

Explanation:
The integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alters their responses by shifting the basis from pretrained internal knowledge to real-time data retrieval. This means that instead of relying solely on the knowledge encoded in the model during training, the LLM can retrieve and incorporate up-to-date and relevant information from an external database in real time. This enhances the model's ability to generate accurate and contextually relevant responses.
Reference
Research papers on Retrieval-Augmented Generation (RAG) techniques
Technical documentation on integrating vector databases with LLMs


NEW QUESTION # 47
ow do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

  • A. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.
  • B. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.
  • C. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
  • D. Dot Product measures the magnitude and direction vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.

Answer: D


NEW QUESTION # 48
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

  • A. Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.
  • B. Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.
  • C. PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning.
  • D. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.

Answer: A

Explanation:
Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT) are two techniques used for adapting pre-trained LLMs for specific tasks.
Fine-tuning:
Modifies all model parameters, requiring significant computing power.
Can lead to catastrophic forgetting, where the model loses prior general knowledge.
Example: Training GPT on medical texts to improve healthcare-specific knowledge.
Parameter-Efficient Fine-Tuning (PEFT):
Only a subset of model parameters is updated, making it computationally cheaper.
Uses techniques like LoRA (Low-Rank Adaptation) and Adapters to modify small parts of the model.
Avoids retraining the full model, maintaining general-purpose knowledge while adding task-specific expertise.
Why Other Options Are Incorrect:
(A) is incorrect because fine-tuning does not train from scratch, but modifies an existing model.
(B) is incorrect because both techniques involve model modifications.
(D) is incorrect because PEFT does not replace the model architecture.
???? Oracle Generative AI Reference:
Oracle AI supports both full fine-tuning and PEFT methods, optimizing AI models for cost efficiency and scalability.


NEW QUESTION # 49
Given the following code: chain = prompt |11m

  • A. LCEL is a legacy method for creating chains in LangChain
  • B. LCEL is a declarative and preferred way to compose chains together.
  • C. LCEL is a programming language used to write documentation for LangChain.
  • D. Which statement is true about LangChain Expression language (ICED?

Answer: C


NEW QUESTION # 50
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