QUIZ 2025 UNPARALLELED 1Z0-1127-24 DUMPS FREE DOWNLOAD & DOWNLOAD ORACLE CLOUD INFRASTRUCTURE 2024 GENERATIVE AI PROFESSIONAL FEE

Quiz 2025 Unparalleled 1z0-1127-24 Dumps Free Download & Download Oracle Cloud Infrastructure 2024 Generative AI Professional Fee

Quiz 2025 Unparalleled 1z0-1127-24 Dumps Free Download & Download Oracle Cloud Infrastructure 2024 Generative AI Professional Fee

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Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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 (Q48-Q53):

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

  • A. 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.
  • B. 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.
  • C. 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.
  • 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: C

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
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

  • A. By incorporating additional layers to the base model
  • B. By allowing updates across all layers of the model
  • C. By restricting updates to only a specific croup of transformer Layers
  • D. By excluding transformer layers from the fine-tuning process entirely

Answer: C


NEW QUESTION # 50
Which Oracle Accelerated Data Science (ADS) class can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment?

  • A. Chain Deployment
  • B. GenerativeAI
  • C. Text Leader
  • D. RetrievalQA

Answer: B


NEW QUESTION # 51
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 # 52
Which is the main characteristic of greedy decoding in the context of language model word prediction?

  • A. It chooses words randomly from the set of less probable candidates.
  • B. It selects words bated on a flattened distribution over the vocabulary.
  • C. It requires a large temperature setting to ensure diverse word selection.
  • D. It picks the most likely word email at each step of decoding.

Answer: D

Explanation:
Greedy decoding in the context of language model word prediction refers to a decoding strategy where, at each step, the model selects the word with the highest probability (the most likely word). This approach is simple and straightforward but can sometimes lead to less diverse or creative outputs because it always opts for the most likely option without considering alternative sequences that might result in better overall sentences.
Reference
Research papers on decoding strategies in language models
Technical documentation on language model inference methods


NEW QUESTION # 53
......

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