CERTIFICATION 1Z0-1127-24 COST & 1Z0-1127-24 VALID DUMPS QUESTIONS

Certification 1z0-1127-24 Cost & 1z0-1127-24 Valid Dumps Questions

Certification 1z0-1127-24 Cost & 1z0-1127-24 Valid Dumps Questions

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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 (Q44-Q49):

NEW QUESTION # 44
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

  • A. Least to most Prompting
  • B. Chain-of-Through
  • C. Step-Bock Prompting
  • D. In context Learning

Answer: B


NEW QUESTION # 45
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

  • A. Emphasis on syntactic clustering of word embedding's
  • B. Improved retrievals for Retrieval Augmented Generation (RAG) systems
  • C. Capacity to translate text in over u languages
  • D. Support for tokenizing longer sentences

Answer: B


NEW QUESTION # 46
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

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

Answer: A

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 # 47
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. After user input but before chain execution, and again after core logic but before output
  • B. Before user input and after chain execution
  • C. Continuously throughout the entire chain execution process
  • D. Only after the output has been generated

Answer: D


NEW QUESTION # 48
Which is NOT a built-in memory type in LangChain?

  • A. Conversation Token Buffer Memory
  • B. Conversation Summary Memory
  • C. Conversation ImgeMemory
  • D. Conversation Buffer Memory

Answer: C


NEW QUESTION # 49
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