1Z0-1127-24 LATEST EXAM COST & 1Z0-1127-24 BEST VCE

1z0-1127-24 Latest Exam Cost & 1z0-1127-24 Best Vce

1z0-1127-24 Latest Exam Cost & 1z0-1127-24 Best Vce

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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
  • 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
  • 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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q61-Q66):

NEW QUESTION # 61
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?
The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

  • A. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
  • B. The level of incorrectness in the models predictions, with lower values indicating better performance
  • C. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
  • D. The improvement in accuracy achieved by the model during training on the user-uploaded data set

Answer: B


NEW QUESTION # 62
Given a block of code:
qa = Conversational Retrieval Chain, from 11m (11m, retriever-retv, memory-memory) when does a chain typically interact with memory during execution?

  • A. Only after the output has been generated
  • B. Before user input and after chain execution
  • C. Continuously throughout the entire chain execution process
  • D. After user input but before chain execution, and again after core logic but before output

Answer: A


NEW QUESTION # 63
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

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

Answer: B

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 # 64
Which is the main characteristic of greedy decoding in the context of language model word prediction?

  • A. It picks the most likely word email at each step of decoding.
  • 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 chooses words randomly from the set of less probable candidates.

Answer: A


NEW QUESTION # 65
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

  • A. When the LLM requires access to the latest data for generating outputs
  • B. When the LLM already understands the topics necessary for text generation
  • C. When you want to optimize the model without any instructions
  • D. When the LLM does not perform well on a task and the data for prompt engineering is too large

Answer: D

Explanation:
Fine-tuning is a technique used to customize an existing Large Language Model (LLM) by training it on domain-specific or task-specific data. Fine-tuning is necessary when:
The LLM's General Knowledge is Insufficient - If the model struggles with a specialized domain (e.g., medical, legal, finance), fine-tuning helps by exposing it to relevant domain-specific data.
Prompt Engineering is Ineffective Due to Large Data Requirements - When a task requires significant custom instructions or examples, fine-tuning is a better approach than prompt engineering, which may have length and complexity limitations.
Improved Accuracy is Required - Fine-tuning helps tailor the model to perform specific tasks more accurately, as it learns from additional training data.
Adapting to a Changing Knowledge Base - Fine-tuning can help update the model with recent trends or company-specific data that were not available during its initial training.
???? Oracle Generative AI Reference:
Oracle supports LLM fine-tuning within its AI ecosystem, allowing enterprises to optimize pre-trained AI models for industry-specific applications.


NEW QUESTION # 66
......

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