ORACLE 1Z0-1127-24 LATEST EXAM BOOK, VALID DUMPS 1Z0-1127-24 FILES

Oracle 1z0-1127-24 Latest Exam Book, Valid Dumps 1z0-1127-24 Files

Oracle 1z0-1127-24 Latest Exam Book, Valid Dumps 1z0-1127-24 Files

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q58-Q63):

NEW QUESTION # 58
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 requires a large temperature setting to ensure diverse word selection.
  • C. It selects words bated on a flattened distribution over the vocabulary.
  • D. It picks the most likely word email at each step of decoding.

Answer: D


NEW QUESTION # 59
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

  • A. It does not update any weights but restructures the model architecture.
  • B. It updates all the weights of the model uniformly.
  • C. It selectively updates only a fraction of the model's weights.
  • D. It increases the training time as compared to Vanilla fine-tuning.

Answer: C

Explanation:
T-Few (Task-Specific Fine-tuning with Few-Shot Learning) is a fine-tuning approach designed to efficiently adapt Large Language Models (LLMs) to new tasks with minimal training data while using a small subset of model weights.
Characteristics of T-Few Fine-Tuning:
Selective Weight Updating: It does not update all model weights but focuses on a small fraction.
Few-Shot Learning Efficiency: Reduces the amount of labeled data required for fine-tuning.
Computational Cost Reduction: Requires significantly less compute than full model fine-tuning.
Better Transferability: Preserves the general knowledge of the base model while adapting to specific tasks.
Why Other Options Are Incorrect:
(B) is incorrect because T-Few updates weights rather than restructuring the model.
(C) is incorrect because not all weights are updated-only a small fraction.
(D) is incorrect because T-Few is optimized for efficiency and does not significantly increase training time.
???? Oracle Generative AI Reference:
Oracle AI supports efficient fine-tuning techniques like T-Few and LoRA (Low-Rank Adaptation) to enhance task-specific performance while reducing computational overhead.


NEW QUESTION # 60
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. RetrievalQA
  • B. GenerativeAI
  • C. Text Leader
  • D. Chain Deployment

Answer: B

Explanation:
The Oracle Accelerated Data Science (ADS) class that can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment is GenerativeAI. This class provides the necessary tools and functions to work with generative AI models, including deployment, fine-tuning, and inference capabilities. It integrates with OCI Data Science to streamline the process of deploying and managing LLM applications.
Reference
Oracle ADS documentation
Guides on deploying AI models using Oracle Data Science services


NEW QUESTION # 61
How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?

  • A. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.
  • B. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.
  • C. Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance.
  • D. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.

Answer: C

Explanation:
In Retrieval-Augmented Generation (RAG), two key evaluation metrics are Groundedness and Answer Relevance, each addressing different aspects of AI-generated responses.
Groundedness (Factual Correctness):
Determines whether the AI-generated answer is factually based on the retrieved data.
Ensures that AI responses are verifiable from the reference documents.
Prevents hallucination, where the model generates plausible but incorrect answers.
Answer Relevance (Query Alignment):
Evaluates how well the response aligns with the user's original query.
A response may be factually correct but irrelevant to the actual question.
Ensures that AI responses remain contextually appropriate.
Key Differences:
Groundedness ensures that responses are based on correct data.
Answer Relevance checks whether the response actually answers the user's query.
???? Oracle Generative AI Reference:
Oracle AI ensures fact-checking and contextual accuracy in its retrieval-augmented models, reducing AI hallucination risks.


NEW QUESTION # 62
Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?

  • A. PEFT involves only a few or new parameters and uses labeled, task-specific data.
  • B. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
  • C. PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies
  • D. PEFT parameters and b typically used when no training data exists.

Answer: A

Explanation:
Parameter-Efficient Fine-Tuning (PEFT) is a technique used in large language model training that focuses on adjusting only a subset of the model's parameters rather than all of them. This approach involves using labeled, task-specific data to fine-tune new or a limited number of parameters. PEFT is designed to be more efficient than classic fine-tuning, which typically adjusts all the parameters of the model. By only updating a small fraction of the model's parameters, PEFT reduces the computational resources and time required for fine-tuning while still achieving significant performance improvements on specific tasks.
Reference
Research papers on Parameter-Efficient Fine-Tuning (PEFT)
Technical documentation on fine-tuning techniques for large language models


NEW QUESTION # 63
......

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